Video classification processing method and apparatus, computer device, and storage medium

Abstract

The present disclosure relates to the technical field of video processing. Disclosed are a video classification processing method and apparatus, a computer device, and a storage medium. The method comprises: acquiring a video set, the video set comprising multiple video samples; for any one video sample in the video set, extracting multiple candidate attribute features of the video sample, wherein the multiple candidate attribute features comprise multiple type sub-features under a text type feature, a picture type feature, and an audio type feature; and on the basis of the multiple candidate attribute features, predicting a classification result of the video sample, and on the basis of the importance of each candidate attribute feature in a classification result prediction process, determining a representative attribute feature from the multiple candidate attribute features, wherein the representative attribute feature is used for determining a classification result of a target video to be processed.

Description

Video classification processing method, device, computer equipment and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on September 26, 2023, with application number 202311255978.8 and invention name “Video classification processing method, device, computer equipment and storage medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present disclosure relates to the field of video processing technology, and in particular to a video classification processing method, apparatus, computer equipment, and storage medium. Background Art

[0004] With the development of computer technology and Internet technology, various multimedia applications have emerged, and users can publish multimedia videos in their favorite multimedia applications for sharing. For multimedia videos published in applications, their quality varies, with some being high-quality videos and some being low-quality videos.

[0005] Currently, the method of selecting high-quality or exquisite videos from multiple videos is mainly through rough screening or manual screening through category labels. However, the accuracy of category label screening is relatively low, and many videos that deviate from the topic will be mixed in. Although manual screening can improve the accuracy to a certain extent, it is inefficient and will introduce a certain degree of subjectivity.

[0006] Summary of the Invention

[0007] In view of this, the embodiments of the present disclosure provide a video classification processing method, apparatus, computer device, and storage medium to solve the problem of reduced efficiency and accuracy in screening high-quality videos.

[0008] In a first aspect, an embodiment of the present disclosure provides a method for processing video classification, comprising: obtaining a video set, the video set including multiple video samples; for any video sample in the video set, extracting multiple candidate attribute features of the video sample; wherein the multiple candidate attribute features include text type features, picture type features, and multiple type sub-features under audio type features; predicting the classification result of the video sample based on the multiple candidate attribute features, and determining a representative attribute feature from the multiple candidate attribute features according to the importance of each candidate attribute feature in the classification result prediction process; wherein the representative attribute feature is used to determine the classification result of the target video to be processed.

[0009] The video classification processing method provided by the embodiment of the present disclosure extracts candidate attribute features of the video sample, screens out representative attribute features from the candidate attribute features, and then automatically classifies the target video to be processed according to the representative attribute features, thereby improving the video screening efficiency and further improving the video classification efficiency. At the same time, it avoids the subjectivity of manual classification and improves the accuracy of video classification.

[0010] In a second aspect, an embodiment of the present disclosure provides a processing device for video classification, comprising: an acquisition module for acquiring a video set, the video set including multiple video samples; a feature extraction module for extracting multiple candidate attribute features of the video sample for any video sample in the video set; wherein the multiple candidate attribute features include text type features, picture type features, and multiple type sub-features under audio type features; a video classification module for predicting the classification results of the video sample based on the multiple candidate attribute features, and determining representative attribute features from the multiple candidate attribute features according to the importance of each candidate attribute feature in the classification result prediction process, wherein the representative attribute features are used to determine the classification results of the target video to be processed.

[0011] In a third aspect, an embodiment of the present disclosure provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the video classification processing method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0012] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the video classification processing method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] FIG1 is a flow chart of a method for processing video classification according to some embodiments of the present disclosure;

[0015] FIG2 is a flow chart of another video classification processing method according to some embodiments of the present disclosure;

[0016] FIG3 is a structural block diagram of a video classification processing apparatus according to an embodiment of the present disclosure;

[0017] FIG4 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present disclosure.

[0019] In related technologies, the method of selecting high-quality or boutique videos from multiple videos is mainly through rough screening or manual screening through category labels. However, the accuracy of category label screening is relatively low, and many videos that deviate from the topic will be mixed in. Although manual screening can improve the accuracy to a certain extent, manual screening is inefficient and will introduce a certain degree of subjectivity.

[0020] Based on this, this technical solution extracts attribute features of video samples in multiple dimensions to mine out more important representative attribute features, and automatically classifies the target video based on the representative attribute features, thereby achieving objectivity and accuracy of video classification.

[0021] According to an embodiment of the present disclosure, an embodiment of a processing method for video classification is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] In this embodiment, a video classification processing method is provided, which can be used in computer devices such as mobile phones and computers. FIG1 is a flow chart of the video classification processing method according to an embodiment of the present disclosure. As shown in FIG1 , the process includes the following steps:

[0023] Step S101: Acquire a video set, which includes multiple video samples.

[0024] A video collection is a collection of video samples with multiple types of labels, each of which contains at least one or more video samples. The type labels of video samples are used to represent the type of content contained in the video samples, such as news, finance, animals, variety shows, etc.

[0025] Specifically, the video collection can be a collection of various types of video samples randomly retrieved from a local video database, or can be pulled from an authorized video platform. Of course, it can also be obtained through other means, which are not specifically limited here.

[0026] Step S102 : extracting multiple candidate attribute features of any video sample in the video set.

[0027] The multiple candidate attribute features include text type features, picture type features, and multiple type sub-features under audio type features.

[0028] The candidate attribute feature is used to characterize the modal feature of the category to which the video sample belongs. Specifically, the candidate attribute feature can be a type sub-feature corresponding to a text type feature, a type sub-feature corresponding to a picture type feature, or a type sub-feature corresponding to an audio type feature.

[0029] Among them, text type features are text features extracted from the text information corresponding to the video sample, including the core theme of the text, the degree of text structuring, the amount of text information, and the richness of vocabulary; picture type features are picture features extracted from the playback video track of the video sample, including picture stability, video resolution, etc.; audio type features are audio features extracted from the audio track of the video sample, including audio quality, the frequency of switching between speaking objects, and the degree of tone fluctuation.

[0030] Step S103 : predicting the classification result of the video sample based on the multiple candidate attribute features, and determining a representative attribute feature from the multiple candidate attribute features according to the importance of each candidate attribute feature in the classification result prediction process.

[0031] The representative attribute features are used to determine the classification results of the target video to be processed.

[0032] The importance level indicates the influence of each candidate attribute feature on the classification result prediction accuracy. That is, if a candidate attribute feature directly affects the classification accuracy of the video sample, it means that the candidate attribute feature is relatively important.

[0033] Specifically, different candidate attribute features have different corresponding importance levels. Here, we can compare multiple type sub-features under text type features, picture type features, and audio type features to extract multiple representative attribute features with high importance rankings.

[0034] Then, when a target video to be processed needs to be classified, the corresponding representative attribute features can be directly extracted from the target video to be processed, and the category to which the target video to be processed belongs can be determined based on the representative attribute features. According to the classification result of the target video to be processed, it is added to the corresponding video content pool.

[0035] The video classification processing method provided in this embodiment extracts candidate attribute features of video samples, screens out representative attribute features from the candidate attribute features, and then automatically classifies the target video to be processed according to the representative attribute features, thereby improving the video screening efficiency and thus improving the video classification efficiency. At the same time, it avoids the subjectivity of manual classification and improves the accuracy of video classification.

[0036] In this embodiment, a video classification processing method is provided, which can be used in computer devices such as mobile phones and computers. FIG2 is a flow chart of the video classification processing method according to an embodiment of the present disclosure. As shown in FIG2 , the process includes the following steps:

[0037] Step S201: Obtain a video set, which includes multiple video samples. Detailed descriptions refer to the corresponding descriptions of the above embodiments, which will not be repeated here.

[0038] Step S202 : For any video sample in the video set, extract multiple candidate attribute features of the video sample.

[0039] The multiple candidate attribute features include text type features, picture type features, and multiple type sub-features under audio type features.

[0040] Specifically, the above step S202 may include:

[0041] Step S2021 , identifying audio data of the video sample, determining text type features represented by the audio data, and extracting multiple text type sub-features from the text type features.

[0042] For any video sample in a video collection, it is composed of a video track and an audio track. When extracting the text type features of a video sample, first, it is necessary to decompose the video track and audio track of the video sample and identify the audio data corresponding to the audio track. Then, the audio data is converted into video content represented by text to obtain text type features related to the video content. Subsequently, the text type features are analyzed to determine the various text type sub-features contained in the text type features, including: whether the text has a core theme, the degree of text structure (logical coherence), the degree of text formality, the amount of text information, the richness of vocabulary, the quality of the text, etc.

[0043] Step S2022 , detecting the picture display state of the video sample, determining the picture type feature of the video sample, and extracting a plurality of picture type sub-features from the picture type feature.

[0044] The screen display state represents the display state of the playback screen of the video sample. For any video sample in the video set, it has a corresponding video playback screen. When playing the video sample, the playback screen of the video sample is captured, and the screen type features related to the playback screen are determined in combination with the screen display state of the playback screen during the playback process. The screen type features are analyzed to determine the various screen type sub-features contained in the screen type features, including: screen stability, whether frequent switching, whether the lens shakes, and video resolution (horizontal and vertical screens).

[0045] Step S2023: Identify audio data in the video sample, determine audio type features that characterize the quality of the audio data, and extract multiple audio type sub-features from the audio type features.

[0046] For any video sample in a video collection, the video and audio tracks of the video sample are decomposed to identify the audio data corresponding to the audio track. The audio data quality is analyzed to determine the audio type features associated with the audio data quality. The audio type features are then analyzed to determine the various audio type sub-features contained within them, including speaker switching frequency and audio quality.

[0047] The video classification processing method provided in this embodiment identifies the features of video samples in various dimensions to determine multiple candidate attribute features possessed by each video sample, thereby enriching the candidate attribute feature extraction of video samples and facilitating the combination of candidate attribute features to determine the impact of each candidate attribute feature and its combined features on the classification of video samples.

[0048] As an optional implementation, before extracting multiple candidate attribute features of the video sample, the above method may further include:

[0049] Step a1, extracting video attribute features of the video sample, where the video attribute features include one or more of playback duration, language, and accent;

[0050] Step a2: Filter the video samples in the video set according to the video attribute features to obtain filtered video samples.

[0051] Video attribute features are used to characterize the playback properties of the current video, namely, playback duration, language, and accent. The playback duration is the total duration from the start to the end of the video sample; the language is the audio language in the video sample, such as Chinese, English, and Russian; and the accent indicates whether the audio data in the video sample contains a regional accent.

[0052] By parsing the video and audio tracks corresponding to the video sample, the start and end times of the video and audio tracks are detected. The earlier start time is determined as the target start time, and the later end time is determined as the target end time. The playback duration of the video sample is the duration between the target start and end times. By parsing the audio track corresponding to the video sample, the corresponding audio data can be determined. By detecting this audio data, the language and accent information contained in the audio data can be determined.

[0053] The video samples in the video collection are filtered based on the video attribute characteristics to eliminate video samples that do not meet the video attribute characteristics, thereby obtaining multiple filtered video samples. For example, if the playback duration is set to N, the playback duration of each video sample in the video collection can be tested to determine whether it has reached the playback duration N, and video samples that do not meet the playback duration can be filtered out. For example, if the language is set to Chinese and does not contain an accent, then by testing the audio data in each video sample, video samples that do not meet the Chinese language requirements and have an accent can be filtered out.

[0054] The video classification processing method provided in this embodiment filters multiple video samples in a video collection through video attribute features, thereby achieving initial screening of video samples, thereby eliminating meaningless video samples and ensuring the classification accuracy of subsequent video samples.

[0055] Step S203 : predicting the classification result of the video sample based on the multiple candidate attribute features, and determining a representative attribute feature from the multiple candidate attribute features according to the importance of each candidate attribute feature in the classification result prediction process.

[0056] The representative attribute features are used to determine the classification results of the target video to be processed.

[0057] Specifically, the above step S203 may include:

[0058] Step S2031: Obtain the actual classification result corresponding to the video sample.

[0059] Each video sample in the video collection has a corresponding type label, and the type label is used to represent the actual classification result of the video sample.

[0060] Step S2032 : comparing the actual classification result with the predicted classification result of the video sample, and determining the classification precision and recall of the video sample based on the comparison result.

[0061] The actual classification result is compared with the classification result of the predicted video sample to determine whether the two are consistent. If the two are consistent, it means that the classification result of the predicted video sample is accurate. Otherwise, it means that the classification result of the predicted video sample is wrong.

[0062] The prediction and classification results for each video sample in the video collection are statistically analyzed to determine the number of video samples with accurate predictions and the number of video samples with incorrect predictions. Combining the total number of video samples in the video collection and the number of accurate predictions, the classification precision and recall of the video samples can be calculated.

[0063] Step S2033: Determine the importance of each candidate attribute feature in the classification result prediction process based on the classification precision and recall.

[0064] The higher the classification precision, the more important the candidate attribute feature. Each candidate attribute feature of a video sample is used as the primary attribute feature of the video sample to predict the classification result, obtaining the corresponding predicted classification result. Combining the predicted classification results, the classification precision corresponding to each candidate attribute feature can be determined. The classification precision represents the importance of the candidate attribute feature in the video sample classification prediction process.

[0065] Step S2034: sort the importance of each candidate attribute feature, and determine a representative attribute feature from the multiple candidate attribute features based on the ranking result of the importance.

[0066] The candidate attribute features corresponding to the video sample are ranked by importance, and a number of representative attribute features with high importance are mined from the candidate attribute features. For example, after ranking the candidate attribute features by importance, the representative attribute features with high importance are determined to be text information content, vocabulary diversity, text topicality, text structure, text formality, and speaker change frequency.

[0067] The video classification processing method provided in this embodiment determines the importance of each candidate attribute feature for the classification of the video sample in combination with the classification precision and recall rate corresponding to the video sample, and mines representative attribute features from the candidate attribute features based on the importance, thereby ensuring the feature mining effect.

[0068] In some optional embodiments, the representative attribute features are determined by a pre-trained video processing model. The video processing model training method includes:

[0069] Step b1: obtaining a video sample set, wherein the video sample set includes a plurality of video samples to be processed, each of which has a corresponding video label, and the video label is used to characterize the classification of the video sample to be processed;

[0070] Step b2: using the video processing model to perform video classification on the candidate attribute features corresponding to any video sample to be processed, so as to output a predicted classification label of the sample to be processed;

[0071] Step b3: comparing the predicted classification label and the video label, and determining the video precision and recall of the video sample to be processed and the classification attribute features corresponding to the video precision and recall based on the comparison result;

[0072] Step b4: calibrate the video processing model based on the video precision and classification attribute features.

[0073] A video sample set is a pre-collected set of video samples to be processed, and includes multiple different types of video samples to be processed. Specifically, the video sample set can be obtained through online collection or by accessing a video database. The method for collecting the video sample set is not specifically limited herein.

[0074] Each video sample has its own corresponding video label. For any video sample to be processed in the video sample set, its video and audio tracks are parsed to extract candidate attribute features of the video sample to be processed. These candidate attribute features are then input into the video processing model to predict the video classification and output the corresponding predicted classification label.

[0075] The predicted classification labels are compared with the video labels to generate a comparison result. The comparison result is used to determine the video recall rate of the video sample to be processed and the classification attribute characteristics corresponding to the video recall rate. The video processing model is then iteratively trained based on the classification attribute characteristics of the video recall rate to achieve correction of the video processing model, making the representative attribute characteristics of the video processing model output more accurate.

[0076] In the above implementation, the video processing model is trained in combination with the classification attribute features of the video precision and recall rate, thereby achieving accurate extraction of representative attribute features of the video samples, and facilitating accurate classification of the video samples to be processed in combination with the representative attribute features.

[0077] In some optional embodiments, the video processing model includes a first sub-model and a second sub-model. The first sub-model is used to generate text content of the video sample to be processed, so as to classify the video sample based on the text content; the second sub-model is used to generate audio content of the video sample to be processed, so as to classify the video sample based on the audio content.

[0078] The video processing model integrates a first sub-model and a second sub-model. The first sub-model can be a binary classification model. Taking the long-text processing models LongFormer and Long-T5 as examples, the real data is first labeled to determine whether the text content has a core theme, is structured, and is formalized. The labels are then used to train a binary classification model based on LongFormer, and a Long-T5 model is trained to determine whether the text content of the video sample to be processed has a core theme, is structured, and is formalized.

[0079] Among them, the second sub-model can be an audio classification model, which is mainly used to parse the video sample to be processed, generate the audio content of the video sample to be processed, and classify the video sample to be processed based on the audio content.

[0080] In the above embodiment, the first sub-model and the second sub-model are respectively used to determine the text classification result and the audio classification result of the video sample to be processed, thereby improving the classification recall effect of the video sample to be processed.

[0081] In some optional embodiments, the above method may further include:

[0082] Step c1: Check whether the target label corresponding to the video sample is in the label vocabulary corresponding to the target category, where the target category is used to represent the video type;

[0083] Step c2: When the target label corresponding to the video sample is in the label vocabulary corresponding to the target category, the classification result of the video sample is determined based on the target label.

[0084] Each target category has multiple levels of labels, such as level 1 labels, level 2 labels, level 3 labels, and level 4 labels. The label vocabulary is a vocabulary consisting of multiple levels of labels for the target category. For any video sample, the target label contained in the metadata information of the video sample is obtained to determine whether the target label is in the label vocabulary. When the target label corresponding to the video sample is in the label vocabulary corresponding to the target category, it means that the video sample belongs to the target category. At this time, the classification result of the video sample can be determined based on the target label and placed in the corresponding video content pool.

[0085] The video classification processing method provided in this embodiment combines the target labels corresponding to the detected video samples and the label vocabulary corresponding to the target categories to determine the classification results of the video samples, further ensuring the classification accuracy of the video samples and enriching the video content pool of various types.

[0086] In some optional embodiments, the above method may further include:

[0087] Step d1, obtaining the knowledge labels corresponding to each video sample;

[0088] Step d2: screening multiple video samples in the video collection based on the knowledge labels to determine the knowledge attributes corresponding to each video sample;

[0089] Step d3: classify the video samples according to the knowledge attributes and determine the classification results of the video samples.

[0090] Knowledge labels are used to characterize whether a video sample is knowledgeable. These labels are manually assigned by watching the video sample. Through these knowledge labels, it can be determined whether the video sample has useful content and a high-value author.

[0091] Each video sample in the video collection is classified according to the knowledge label to determine whether the video sample has the knowledge attribute. The knowledge attribute is used to characterize the content type of the video sample. Thus, by classifying each video sample according to the knowledge attribute, the content type of each video sample can be determined, and the classification result for the content type is obtained and placed into the corresponding video content pool.

[0092] The video classification processing method provided in this embodiment combines knowledge tags to determine the content type of the video sample, thereby achieving classification results for the video sample and further enriching the video content pool of various types.

[0093] In this embodiment, a video classification processing device is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments. The details already described will not be repeated here. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0094] This embodiment provides a video classification processing device, as shown in FIG3 , including:

[0095] The acquisition module 301 is configured to acquire a video set, where the video set includes multiple video samples.

[0096] The feature extraction module 302 is used to extract multiple candidate attribute features of any video sample in the video set, wherein the multiple candidate attribute features include text type features, picture type features, and multiple type sub-features under audio type features.

[0097] The video classification module 303 is used to predict the classification results of the video sample based on multiple candidate attribute features, and determine the representative attribute features among the multiple candidate attribute features according to the importance of each candidate attribute feature in the classification result prediction process, wherein the representative attribute features are used to determine the classification results of the target video to be processed.

[0098] In some optional embodiments, the feature extraction module 302 may include:

[0099] The text feature determination unit is used to identify the audio data of the video sample, determine the text type features represented by the audio data, and extract multiple text type sub-features from the text type features.

[0100] The picture feature determination unit is used to detect the picture display state of the video sample, determine the picture type feature of the video sample, and extract multiple picture type sub-features from the picture type feature.

[0101] The audio feature determination unit is used to identify audio data in the video sample, determine the audio type feature that characterizes the quality of the audio data, and extract multiple audio type sub-features from the audio type feature.

[0102] In some optional embodiments, the above device may further include:

[0103] The attribute feature extraction module is used to extract video attribute features of the video sample, where the video attribute features include one or more of playback duration, language, and accent.

[0104] The filtering module is used to filter the video samples in the video set according to the video attribute characteristics to obtain filtered video samples.

[0105] In some optional embodiments, the video classification module 303 may include:

[0106] The actual classification acquisition unit is used to obtain the actual classification result corresponding to the video sample.

[0107] The comparison unit is used to compare the actual classification result with the predicted classification result of the video sample, and determine the classification precision and recall rate of the video sample based on the comparison result.

[0108] The importance determination unit is used to determine the importance of each candidate attribute feature in the classification result prediction process based on the classification precision and recall.

[0109] The ranking unit is used to rank the importance of each candidate attribute feature, and determine a representative attribute feature from multiple candidate attribute features based on the ranking result of the importance.

[0110] In some optional embodiments, the representative attribute features are determined by a pre-trained video processing model. Accordingly, the above apparatus may further include:

[0111] Model training module, used to train video processing models.

[0112] Specifically, the above-mentioned model training module is used to: obtain a video sample set, which includes multiple video samples to be processed, and the video samples to be processed have corresponding video labels, and the video labels are used to characterize the classification of the video samples to be processed; use the video processing model to perform video classification on the candidate attribute features corresponding to any video sample to be processed to output the predicted classification label of the sample to be processed; compare the predicted classification label and the video label, and determine the video recall rate of the video sample to be processed and the classification attribute features corresponding to the video recall rate based on the comparison results; calibrate the video processing model through the video recall rate and the classification attribute features.

[0113] In some optional embodiments, the video processing model includes a first sub-model and a second sub-model. The first sub-model is used to generate text content of the video sample to be processed, so as to classify the video sample based on the text content; the second sub-model is used to generate audio content of the video sample to be processed, so as to classify the video sample based on the audio content.

[0114] In some optional embodiments, the above device may further include:

[0115] Category detection module, used to detect whether the target label corresponding to the video sample is in the label vocabulary corresponding to the target category, which is used to characterize the video type;

[0116] The label classification module is used to determine the classification result of the video sample based on the target label when the target label corresponding to the video sample is in the label vocabulary corresponding to the target category.

[0117] In some optional embodiments, the above device may further include:

[0118] A knowledge label acquisition module is used to obtain the knowledge label corresponding to each video sample;

[0119] The screening module is used to screen multiple video samples in the video collection based on knowledge tags and determine the knowledge attributes corresponding to each video sample.

[0120] The knowledge classification module is used to classify video samples according to knowledge attributes and determine the classification results of the video samples.

[0121] The video classification processing device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0122] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0123] The video classification processing device provided by the embodiment of the present disclosure extracts candidate attribute features of the video sample, screens out representative attribute features from the candidate attribute features, and then automatically classifies the target video to be processed according to the representative attribute features, thereby improving the video screening efficiency and further improving the video classification efficiency. At the same time, it avoids the subjectivity of manual classification and improves the accuracy of video classification.

[0124] The embodiment of the present disclosure further provides a computer device having the video classification processing device shown in FIG3 .

[0125] Please refer to Figure 4, which is a structural diagram of a computer device provided by an optional embodiment of the present disclosure. As shown in Figure 4, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 takes a processor 10 as an example.

[0126] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0127] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0128] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0129] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0130] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0131] The embodiments of the present disclosure also provide a computer-readable storage medium. The above-mentioned method according to the embodiments of the present disclosure can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0132] Although the embodiments of the present disclosure have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A video classification processing method, comprising: Acquire a video set, wherein the video set includes a plurality of video samples; For any video sample in the video set, extract multiple candidate attribute features of the video sample; wherein the multiple candidate attribute features include a text type feature, a picture type feature, and multiple type sub-features under an audio type feature; Predicting a classification result of the video sample based on the multiple candidate attribute features, and determining a representative attribute feature from the multiple candidate attribute features according to the importance of each of the candidate attribute features in the classification result prediction process; The representative attribute feature is used to determine the classification result of the target video to be processed.

2. The method according to claim 1, wherein the extracting a plurality of candidate attribute features of the video sample comprises: Identifying audio data of the video sample, determining the text type feature represented by the audio data, and extracting a plurality of text type sub-features from the text type feature; and / or, Detecting a picture display state of the video sample, determining a picture type feature of the video sample, and extracting a plurality of picture type sub-features from the picture type feature; and / or, The audio data in the video sample is identified, an audio type feature characterizing the quality of the audio data is determined, and a plurality of audio type sub-features are extracted from the audio type feature.

3. The method according to claim 1 or 2, wherein before extracting a plurality of candidate attribute features of the video sample, it further comprises: Extracting video attribute features of the video sample, wherein the video attribute features include one or more of playback duration, language, and accent; The video samples in the video set are filtered according to the video attribute features to obtain filtered video samples.

4. The method according to claim 1, wherein determining a representative attribute feature from the plurality of candidate attribute features according to the importance of each of the candidate attribute features in the classification result prediction process comprises: Obtaining actual classification results corresponding to the video samples; Comparing the actual classification result with the predicted classification result of the video sample, and determining the classification precision and recall rate of the video sample based on the comparison result; Determining the importance of each of the candidate attribute features in the classification result prediction process based on the classification precision and recall; The importance of each of the candidate attribute features is ranked, and the representative attribute feature is determined from the plurality of candidate attribute features based on the ranking result of the importance.

5. The method according to claim 1 or 4, wherein the representative attribute feature is determined by a pre-trained video processing model; The training method of the video processing model includes: Acquire a video sample set, wherein the video sample set includes a plurality of video samples to be processed, and the video samples to be processed have corresponding video tags, and the video tags are used to characterize the classification of the video samples to be processed; Using the video processing model to perform video classification on the candidate attribute features corresponding to any of the video samples to be processed, so as to output a predicted classification label of the sample to be processed; Comparing the predicted classification label with the video label, and determining the video precision and recall of the video sample to be processed and the classification attribute characteristics corresponding to the video precision and recall based on the comparison result; The video processing model is calibrated according to the video precision-recall rate and the classification attribute characteristics.

6. The method of claim 5, wherein the video processing model comprises a first sub-model and a second sub-model; in, The first sub-model is used to generate text content of the video sample to be processed, so as to classify the video sample to be processed based on the text content; The second sub-model is used to generate audio content of the video sample to be processed, so as to classify the video sample to be processed based on the audio content.

7. The method according to claim 1, further comprising: Detecting whether a target tag corresponding to the video sample is in a tag vocabulary corresponding to a target category, where the target category is used to characterize a video type; When the target tag corresponding to the video sample is in the tag vocabulary corresponding to the target category, the classification result of the video sample is determined based on the target tag.

8. The method according to claim 1, further comprising: Obtaining knowledge labels corresponding to each of the video samples; Screening a plurality of video samples in the video set based on the knowledge tags to determine knowledge attributes corresponding to each of the video samples; The video samples are classified according to the knowledge attributes to determine the classification results of the video samples.

9. A video classification processing device, comprising: An acquisition module, used to acquire a video set, wherein the video set includes a plurality of video samples; A feature extraction module, configured to extract a plurality of candidate attribute features of any video sample in the video set; wherein the plurality of candidate attribute features include a text type feature, a picture type feature, and a plurality of type sub-features under an audio type feature; A video classification module is used to predict the classification result of the video sample based on the multiple candidate attribute features, and determine a representative attribute feature from the multiple candidate attribute features according to the importance of each candidate attribute feature in the classification result prediction process, wherein the The representative attribute features are used to determine the classification result of the target video to be processed.

10. A computer device comprising: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the video classification processing method according to any one of claims 1 to 8 by executing the computer instructions.

11. A computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to enable a computer to execute the video classification processing method according to any one of claims 1 to 8.

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