1827results about "Metadata still image retrieval" patented technology

The invention discloses a transformer substation engineering construction drawing intelligent analysis and evaluation system based on image recognition, and relates to the technical field of transformer substation engineering construction informatization. The data processing center is in communication connection with a data acquisition layer, an image preprocessing layer, a feature extraction layer, an intelligent analysis layer, an evaluation decision-making layer and an application display layer, and all layers of architectures are in electric signal connection. According to the method, the improved convolutional neural network model is adopted, and an image preprocessing algorithm specially aiming at engineering drawing feature optimization is combined, so that the drawing recognition accuracy is remarkably improved compared with a traditional OCR technology, the manual proofreading workload is greatly reduced, a drawing element correlation analysis system based on a knowledge graph is constructed, and the engineering drawing recognition accuracy is improved. Key information such as equipment arrangement, line connection and size marking in the drawing can be automatically extracted and associated, and structural storage and intelligent retrieval of drawing content are achieved.

The invention relates to the technical field of civil engineering informatization and automation design, in particular to a parameterized construction drawing generation method based on a constraint solution algorithm, and the method comprises the steps: defining a construction drawing foundation structure and variable parameters through an engineering design model embedded with a parameterized template; a parameter constraint relation set is generated in combination with geometric constraints, size constraints and logic constraints input by a user, constraint rules are converted into intermediate expressions through a formula analyzer, a syntax tree is constructed, and an executable parameter association rule set is generated through semantic verification. The auditing module verifies the drawing compliance based on the engineering specification database and generates a correction suggestion; and realizing cross-system data linkage between the multi-terminal collaborative interface and external BIM model parameter mapping. According to the method, the problems of detail loss and specification deviation in three-dimensional model projection are effectively avoided, and the drawing generation efficiency and the multi-specialty collaborative robustness are improved.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.

The invention relates to a hospital database management method and system based on adaptive data desensitization. The method comprises the following steps: S1, uniformly integrating multi-source data of a hospital into a database; s2, performing sensitivity grading on the data of the database through a data classification algorithm, and automatically generating corresponding labels; s3, establishing a desensitization model based on role permission, access content and data features; s4, the user inquires data and inputs related parameters through the front-end system, whether fields or data in the request contain high-sensitivity fields or not is judged, and if the fields or the data contain the high-sensitivity fields, a proper desensitization strategy is dynamically generated based on a desensitization model according to the user role, the data sensitivity and the access context; and S5, monitoring access behaviors in combination with data using log records, carrying out anomaly detection based on a GANopen model, and if an abnormal behavior is detected, triggering an alarm or stopping access. According to the invention, efficient utilization and sharing of data are realized, and the intelligent level of hospital data management is improved.

A method of automated generation of contextually-relevant images for a music segment includes receiving at least one of basic metadata information and lyric information for the music segment, generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, receiving context information from the computer-implemented machine-learning language model in response to the first prompt, generating a second prompt for the computer-implemented machine-learning language model based on the context information, generating a third prompt by providing the second prompt as an input to the computer-implemented machine-learning language model, and generating an image descriptive of the music segment by providing the third prompt as an input to a computer-implemented machine-learning image generation model.

A method includes obtaining, by one or more processors, a plurality of images, executing, by the one or more processors, a domain-specific target audience machine learning model to generate domain performance scores for the plurality of images, ranking, by the one or more processors, the plurality of images according to the domain performance scores for the plurality of images, and generating, by the one or more processors, a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.

The invention discloses an image cross-modal retrieval method and device based on a large language model and a medium, and the method comprises the steps: generating a text description corresponding to an original image through a BLIP model, and converting the text description into a corresponding image vector; establishing a mapping relationship between the image vector and the original image, and storing the mapping relationship and the image vector to a preset vector database; obtaining an image query description submitted by a user, optimizing the image query description through a preset language large model, and converting the optimized image query description into a corresponding query vector; determining a plurality of to-be-selected images corresponding to the image query description according to a first similarity between each image vector and the query vector in a vector database; and calculating a matching degree between the image query description and the plurality of to-be-selected images, and screening out a target image meeting the image query description from the plurality of to-be-selected images according to the matching degree.

The invention discloses a defect file and image number association method based on multi-format analysis, and relates to the technical field of semiconductor detection and data analysis, and the method comprises the following steps: constructing a multi-format rule base, receiving batch Klarf files and corresponding defect image folders, and extracting image numbers in defect image file names; reading field names of a preset number of Defect data of the Klarf file, matching the field names with the multi-format rule base, and selecting an analysis strategy according to a matching result by adopting a self-adaptive analysis algorithm; the analyzed Defect data are preprocessed, the association confidence coefficient is calculated through a three-layer verification algorithm, and an association result table is generated; according to the invention, automation, standardization and high reliability of semiconductor defect detection data processing are realized.

The invention provides a similar picture retrieval method, which comprises the following steps of: constructing a hierarchical semantic-visual representation framework, and decomposing query and images into three levels, namely a core concept, attribute modification and relation description; a context-aware professional term understanding system is achieved, the professional field background and purpose of query are analyzed, context feature vectors are generated, and term ambiguity is eliminated; establishing a semantic-vision bidirectional mapping and optimization framework, realizing forward mapping from semantics to vision and reverse mapping from vision to semantics, and generating an optimization mapping matrix and a candidate set through iterative optimization; and performing multi-modal similarity calculation, converting the context feature vector into a hierarchical weight vector, fusing the hierarchical weight vector with the multi-level similarity into a comprehensive similarity score, and sorting and outputting a retrieval result set according to the comprehensive similarity score. According to the method, the accuracy of similar picture retrieval in the professional field is improved.

The present technology provides for AI-assisted video editing tools that implement a novel method of processing video which includes transcribing segments of a video and labeling the segments based on the contents of the segments. A smart edit can be generated based on the labelled segments. The smart edit can serve as a rough cut for a user to make further edits.

A method and apparatus for generating photorealistic synthetic images by receiving multiple forms and multiple instances of user input corresponding to a user's visual idea, executing an iterative image search to identify pre-existing images semantically aligned with the user's visual idea, and using an image synthesis deep learning model to generate at least one synthetic image based on the multiple forms and instances of user inputs.

The invention provides a multi-modal geographic positioning method based on a knowledge graph. The method comprises the following steps: graph construction: constructing a geographic knowledge graph; image positioning: calculating the entity similarity between the to-be-queried image and each sub-image so as to select an image positioning candidate sub-image, calculating the image matching similarity between the to-be-queried image and each image positioning candidate sub-image so as to determine an image positioning target sub-image, and taking the latitude and longitude coordinates of each image positioning target sub-image as a positioning result; text positioning: converting a to-be-queried text into map representation, calculating the map similarity between a text query sub-graph and each sub-graph to select a text positioning candidate sub-graph, and calculating the semantic similarity between the to-be-queried text and each text positioning candidate sub-graph, and calculating a text matching similarity corresponding to each text positioning candidate sub-graph based on the graph similarity and the semantic similarity corresponding to each text positioning candidate sub-graph so as to determine a text positioning target sub-graph, and taking latitude and longitude coordinates of each text positioning target sub-graph as a positioning result.

The invention relates to the technical field of cross-view image positioning, in particular to a cross-view image geographic positioning method and system for an unmanned aerial vehicle and a satellite image, and the method comprises the steps: employing a Dinov2 large model after Conv-LoRA fine adjustment as a feature encoder, and extracting an unmanned aerial vehicle image feature vector and a satellite image feature vector; designing a spatial relation perception feature aggregator MSRA based on a Mama module for aggregating image features, and embedding spatial configuration features into a global descriptor; an InfoNCE loss function is adopted to train the model; and after feature vectors of a to-be-queried image and each image in the reference image library are extracted by using the trained model, a similarity score is calculated by using cosine similarity, and the unmanned aerial vehicle-satellite image pair with a high matching degree is selected to realize geographic positioning of the to-be-queried image. According to the method, Conv-LoRA is adopted to carry out fine tuning on a Dinov2 large model, so that the quantity of parameters needing to be trained is greatly reduced; a spatial configuration relationship between features is fully mined through a spatial relationship perception feature aggregator based on a Mama module.

The invention provides a noise robust text-to-image pedestrian retrieval method and device based on a soft label. The method comprises the following steps: calculating cosine similarity based on image global features and text global features, and normalizing the cosine similarity to generate a soft label representing image-text pairing confidence; distributing a sample weight for each training sample according to the soft label, and obtaining a joint weight for current iteration in combination with a dynamic weight factor progressively increased along with the training process; respectively constructing cross-modal contrast learning loss and similarity distribution matching loss by using the joint weight, and carrying out weighted summation on the cross-modal contrast learning loss and the similarity distribution matching loss to obtain a total loss function; and updating parameters of the image encoder and the text encoder by using the total loss function until training convergence, and obtaining a cross-modal alignment model. According to the method, robust cross-modal alignment can be realized, and the pedestrian retrieval accuracy in a noise scene is improved.

The invention relates to the technical field of image retrieval, in particular to an image retrieval method based on big data mining, which comprises the following steps: collecting images and metadata, screening metadata fields, determining a candidate image set and obtaining a metadata screening result, according to the method, in the image retrieval process, image metadata screening, geographic information clustering, visual feature fusion, index construction and matching strategy optimization are combined, and the retrieval accuracy and response efficiency are improved. In the metadata screening link, data is screened by using information such as time, places and equipment types, so that interference of irrelevant data is reduced, and a candidate image set is more targeted. Geographic information and timestamps are combined for classification, and clustering analysis is performed by utilizing a geographic information system, so that spatial distribution information plays a greater role in a retrieval process, and a more accurate result can be obtained for query of a specific region.

The invention discloses a multi-modal agricultural question and answer method and system for generating RAG based on retrieval enhancement, and the method comprises the steps: collecting and constructing crop disease image data containing a farmland complex background and corresponding text description, and forming an agricultural image-text knowledge database; based on the agricultural image-text knowledge database, multi-modal features of an input image and a query text are extracted, image-text joint similarity retrieval is carried out, and knowledge image-text candidates are obtained; inputting the knowledge image-text candidates into an image-text rearrangement module for rearrangement; taking the reordered image-text knowledge as condition input, accessing a large language model, and generating diagnosis description and prevention and treatment suggestions for the current crop diseases; and outputting a multi-modal question and answer result according to the image and question input by the user. According to the method, the agricultural image-text database is constructed, and multi-modal retrieval, rearrangement and large language model generation technologies are combined, so that accurate and efficient crop disease multi-modal question answering is realized, and the reliability of diagnosis and prevention suggestions is improved.

The invention belongs to the technical field of image-text retrieval, and discloses a cross-modal image-text retrieval method based on a multi-window attention mechanism, which comprises the following steps: S1, data preprocessing and embedding: preprocessing an image and a text, and converting the image and the text into a vector matrix; s2, image and text feature extraction: for the image vector matrix, extracting local and global features of image blocks by using a Transform module based on a multi-window attention mechanism to obtain a visual feature matrix; extracting text features from the text vector matrix; s3, performing cross-modal semantic alignment and semantic similarity calculation: performing cross-modal semantic alignment on the visual feature matrix and the text feature matrix, and obtaining a fine-grained semantic relationship matrix of the image blocks and the text words by adopting a cross-mapping alignment network; mapping representation and semantic alignment between the two modes are achieved, and the semantic similarity of the image and the text is calculated; and S4, outputting a retrieval result. Through the method, the image-text matching and retrieval effects are improved.

The invention relates to the technical field of image retrieval, and discloses a multi-modal image retrieval method, system and device, a medium and a program product, the method comprises the following steps: obtaining a to-be-retrieved image and / or character description information of the image, inputting the to-be-retrieved image and / or the character description information of the image into a pre-constructed multi-modal image retrieval model, and taking a feature vector output by the model as a query vector; performing similarity calculation on the query vector and a feature vector corresponding to each image in a preset image library, and taking the image of which the similarity is greater than a preset threshold as a matched image; and displaying the matched images as retrieval results according to the sequence of the similarity from high to low. The multi-mode image retrieval method can provide retrieval modes of searching images by text, searching images by images and searching images by image-text mixing, realizes multi-mode image retrieval capability, provides ideas of attribute learning and sorting learning, realizes image retrieval of information of other dimensions except categories, adapts to the characteristic of complexity of an image retrieval task, and improves the image retrieval efficiency. And the convenience, accuracy and robustness of image retrieval are improved.

In other embodiments, a method to perform image-based searching may include receiving a group of images from a content owner; creating an index using the group of images; receiving a further image from a mobile computing device of a user; determining a correlation between the further image and the index; and / or directing the mobile computing device of the user to content of the content owner, responsive to the determining of the correlation.

A condition generator analyzes a set of utility asset images of a particular utility asset using the ML (machine learning) model identify a type and condition of the particular utility asset depicted in the set of utility asset images. The identified condition is assigned a confidence score, and the set of utility asset images includes at least two images of the particular utility asset captured at different angles. The condition generator generates a descriptive tag for the set of utility asset images based on the identified type and condition. The descriptive tag characterizes an operational status of the particular utility asset. The condition generator stores the set of utility asset images and the generated descriptive tag in a utility asset database. The utility asset database stores images of utility assets.

The invention discloses a cross-domain expression motion unit detection method based on text bridging, and the method comprises the steps: extracting AU visual features of different scales through employing a visual encoder, introducing a group of learnable prompt vectors, and combining with AU text description to obtain AU text features; the source domain features and the text features are aligned through comparative learning, and the discrimination of the source domain AU visual features is improved; a cross-modal attention mechanism is adopted to realize preliminary interaction of vision and texts, and AU interaction feature representation is obtained; further promoting deep interaction and fusion of the text and the visual information through a graph neural network to obtain a fused AU feature; and on the basis of a negative sample AU description-based comparative learning method, target domain features are aligned with text features, and feature distribution differences between a source domain and a target domain are reduced. According to the method, the field invariance of the text is utilized, the visual features of the source domain and the target domain are unified, the text features are aligned, and the generalization performance of an AU detection model and the accuracy of the AU detection model on the target domain are remarkably improved.

The invention discloses a remote sensing image searching and matching method and device based on feature fusion, and relates to the technical field of remote sensing, a test image of an unmanned aerial vehicle or a heterogenous satellite is acquired, a feature library is established after preprocessing, and feature extraction and index establishment are performed on each base map in the base map library. And selecting a retrieval strategy according to the size of the test image, retrieving the name of the base image and cutting a related grid image. The method comprises the following steps: converting a test and grid image into gray scale and zooming, extracting feature points to form a set, estimating an affine transformation matrix by using a random sampling consensus algorithm, and preliminarily evaluating the number of matched inner points; if the first preset threshold value is exceeded, executing a fine matching step; and if the number of points in the fine matching step exceeds a second preset threshold value, returning to match the name of the base map and the longitude and latitude of the angular points. And if not, iterating precise matching until an optimal matching result and the longitude and latitude of the angular point are returned. The problem of how to efficiently and accurately find an image of a specific area for massive and multi-source remote sensing image data is solved.

The present disclosure provides a large model-based search method and system, a device and a medium, which are applied to the technical fields of artificial intelligence and intelligent cameras. The method comprises: storing a plurality of information pairs into a database, each information pair comprising an acquired image and descriptive text information used for describing the acquired image, and the descriptive text information being generated by using a pre-trained visual-semantic large model to process the acquired image after an intelligent camera acquires the acquired image; in response to receiving a search request, analyzing search information from the search request; and determining from the database a search result matched with the search information, the search result at least comprising one information pair. The large model-based search method provided by the present invention can improve search efficiency, thus improving user experience.

The invention relates to the technical field of trademark image retrieval, and discloses a trademark image retrieval method and system based on deep learning, and the method comprises the steps: obtaining a trademark image; scoring the trademark image to obtain image complexity; according to the trademark image and the image complexity, wavelet transformation is carried out to obtain a feature vector, dynamic adjustment is carried out, and feature representation is obtained; according to the feature representation, inputting the feature representation into a preset semantic association model to obtain candidate association information; according to the candidate association information, calculating the similarity between the image semantic vector and the brand node by using a knowledge graph reasoning technology, and obtaining an association result based on a similarity threshold value; dynamically generating a text label of the trademark image according to an association result, and mapping the text label and an entity in the brand knowledge base by adopting an entity linking technology to obtain a mapping result; and inputting the mapping result into a preset semantic link network to obtain a trademark retrieval result. According to the method, the accuracy of trademark image retrieval can be improved.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

The invention discloses a CAD drawing instant positioning method and device based on spatial index analysis, and relates to the related technical field of CAD drawing positioning, and the method comprises the steps: carrying out the multi-stage splitting identification of a multi-version target CAD drawing; performing associated indexing on the drawing version information, the drawing layer information and the drawing component set; semantic analysis and spatial association are carried out on the drawing text annotations; embedding and associating the drawing component space semantic tag set into a component-layer-drawing three-level association graph; performing matching indexing on the drawing semantic query request based on the drawing spatial index mapping table; and inquiring a topology path based on the component to carry out instant positioning and visual feedback. The technical problems that in the prior art, complex spatial relations and semantic information in the drawings are difficult to effectively process, and multi-version CAD drawings are low in retrieval efficiency, insufficient in instantaneity and inaccurate in positioning are solved, and the technical effect of improving the retrieval efficiency, query instantaneity and positioning accuracy of the multi-version drawings is achieved.

The present application provides a task processing method and a server. According to the method of the present application, mixed pre-training data in the vertical field and the general field is used to continue pre-training a pretrained model to obtain a pre-trained domain foundation model; on the basis of keeping original strong semantic understanding and representation capability of the pre-trained model as much as possible, the model is made to be familiar with the characteristics of the text format and data distribution in the vertical field, and more knowledge of the vertical field is injected into the model, thereby enhancing the text understanding and generation capability of the model in the vertical field; then a data set in the vertical field is used to perform fine tuning training on the domain foundation model to obtain a domain model suitable for the vertical field, so that a domain model general for various natural language processing (NLP) tasks in the vertical field can be obtained, and the text understanding and generation capability of the model in the vertical field is improved, thereby improving the generation quality of task processing results when the model is applied to various NLP tasks in the vertical field.

The invention relates to the technical field of geographic positioning, in particular to a streetscape image geographic positioning method and system based on cross-modal comparative learning and description enhancement, and the method comprises the steps: obtaining the visual features of a to-be-queried streetscape image and a feature set of a reference image library through a pre-trained image encoder; generating text description features of the to-be-queried streetscape image by using a large language model, and fusing the visual features and the text description features to generate a query feature set of the to-be-queried streetscape image; aligning the to-be-queried streetscape image with the reference image based on the query feature set and the image library feature set to obtain a geographic position of the to-be-queried streetscape image; wherein the image encoder is trained based on the streetscape image sample set and the reference image sample set in the training process by using a preset cross-modal contrast learning loss function. The method can solve the problem of non-panoramic image positioning, improves the accuracy and flexibility of the positioning process, and has a good application prospect in the field of geographic positioning.

Systems and methods for image search result filtering can include obtaining a search query, determining a plurality of candidate image search results, processing the search query with a generative model to determine a plurality of search result criteria, and refining the plurality of candidate image search results based on determining whether the candidate results satisfy the plurality of search results criteria. The systems and methods can perform a plurality of determinations based on the output of the generative model.