114 results about "Learning by example" patented technology

The invention belongs to the technical field of computer vision and natural language processing, and discloses a visual salience and semantic attribute based cross-modal image natural language description method. The method comprises the steps that multiscale deep visual features of all regions are extracted by adopting a convolutional neural network; by means of a pre-trained significance model,an image significance graph is returned, and an original image is weighted; a predefined dictionary is built to serve as a semantic attribute category, and semantic attribute detection is conducted ona visual significance image; semantic attributes are calculated through multi-instance learning; image features are weighted through the semantic attributes; visual-salience-based semantic attributefeatures are decoded through a long short-term memory network, and image description is generated. The method has the advantage of being high in accuracy and can be used for image retrieval under complex scenes, multi-objective image semantic understanding and the like.

The invention discloses a network violent video identification method based on multiple examples and multiple characteristics. The method for identifying the network violent videos comprises the steps of grasping violent videos, non-violent videos, comments on the violent videos, comments on the non-violent videos, brief introductions of the violent videos and brief inductions of the non-violent videos from a video sharing network, and structuring a video data training set; extracting textural characteristics from textural information of the training set, forming textural characteristic vectors to train a textural pre-classifier, and screening out candidate violent videos by using the pre-classifier; using a shot segmentation algorithm based on a self-adapting dual threshold for conducting segmentation on video segments of the candidate violent videos, extracting related visual characteristics and voice frequency characteristics of each scene to express the scene, taking each scene as an example of multi-example study, and taking video segments as a package; and using an MILES algorithm for converting the package into a single example, using a characteristic vector for training a classifier model, and using the classifier model for conducting classification on the candidate violent videos. By the utilization of the network violence video identification method, bad influences that the network violent videos are broadcasted without constrain are largely lightened.

The invention discloses a multi-kernel support vector machine multi-instance learning algorithm applied to pedestrian re-identification. The algorithm includes two main steps, namely multi-feature description and a multi-kernel SVM model multi-instance learning algorithm. According to the algorithm, HSV color features and SIFT local features of two pictures, taken under a camera A and a camera B, of the same pedestrian are extracted to construct a word bag, and difference vectors of the two kinds of the features represent the conversion relation under the two cameras to serve as two instance samples and are encapsulated as a bag; then a multi-kernel support vector machine model is optimized, the bag is trained by means of linear fusion of the Gaussian kernel and a polynomial kernel, optimal parameters are obtained through multi-instance learning, and a high identification rate is achieved.

A system and method are provided for deep high-order exemplar learning of a data set. Feature vectors and class labels are received. Each of the feature vectors represents a respective one of a plurality of high-dimensional data points of the data set. The class labels represent classes for the high-dimensional data points. Each of the feature vectors are processed, using a deep high-order convolutional neural network, to obtain respective low-dimensional embedding vectors within each class. A minimization operation is performed on high-order embedding parameters of the high-dimensional data points to output a set of synthetic exemplars. A binarizing operation is performed on the low-dimensional embedding vectors and the set of synthetic exemplars to output hash codes representing the data set. The hash codes are utilized as a search key to increase the efficiency of a processor-based machine searching the data set.

The invention belongs to the technical field of image processing and particularly provides a semantic propagation and mixed multi-instance learning-based Web image retrieval method. Web image retrieval is performed by combining visual characteristics of images with text information. The method comprises the steps of representing the images as BoW models first, then clustering the images according to visual similarity and text similarity, and propagating semantic characteristics of the images into visual eigenvectors of the images through universal visual vocabularies in a text class; and in a related feedback stage, introducing a mixed multi-instance learning algorithm, thereby solving the small sample problem in an actual retrieval process. Compared with a conventional CBIR (Content Based Image Retrieval) frame, the retrieval method has the advantages that the semantic characteristics of the images are propagated to the visual characteristics by utilizing the text information of the internet images in a cross-modal mode, and semi-supervised learning is introduced in related feedback based on multi-instance learning to cope with the small sample problem, so that a semantic gap can be effectively reduced and the Web image retrieval performance can be improved.

The invention relates to the technical field of industrial surface defect detection, and provides a plate strip steel surface defect detection method based on a significance label information propagation model. The method comprises the following steps of firstly, acquiring a plate strip steel surface image I; then, extracting a bounding box from the image I, and executing a bounding box selectionstrategy; then, performing super-pixel segmentation on the image I, and extracting a feature vector from each super-pixel; then, constructing a significance label information propagation model, constructing a training set based on a multi-example learning framework to train a classification model based on a KISVM, classifying a test set by using the trained model to obtain a category label matrix,calculating a smooth constraint item and a high-level prior constraint item, and optimizing and solving a diffusion function; and finally, calculating a single-scale saliency map under multiple scales, and obtaining a final defect saliency map through multi-scale fusion. The surface defects of the strip steel can be efficiently, accurately and adaptively detected, a complete defect target can beuniformly highlighted, and a non-significant background area can be effectively inhibited.

The invention relates to a method for detecting specific contained semantics of a video based on a grouped multi-instance learning model in the technical field of computer video treatment, which comprises the following steps: continuously cutting the video according to the shots, thereby acquiring a plurality of video segments; using a FFMPEG tool to intercept image describers for each video segment Sij, wherein averagely 25 pictures are intercepted from each video segment at the same interval; extracting the related audio describers by using a video audio track, intercepting the video describers by using a video screenshot set, and intercepting the motion degree by using the video; performing machine learning on each set of describers; and acquiring a result after performing the machine learning, performing an European distance calculation on the learning result and one describer of one target video, and using the acquired minimum value as the approaching degree of the original video under the description of the describer for the target video.

The invention discloses an image multi-tag marking algorithm based on multi-example package feature learning, and the algorithm comprises the steps: obtaining a set of image blocks of all training images; extracting the features of a color histogram and the features of a direction gradient histogram of each image block of the set of the training images; enabling one training image to serve as an image package, and obtaining an image package structure needed by a multi-example learning framework; enabling the examples in all image packages in the set to form a projection example set, enabling each image package to be projected towards the projection example set, and obtaining the projection features of the image packages; selecting the features with the high discrimination performance as the classification features of the image packages; importing the classification features of the image packages of the learned training image set into an SVM classifier for training, obtaining the parameters of a training model, and predicting a test image tag through employing a trained SVM classifier. The algorithm is simple in implementation, and a trainer is mature and reliable. The algorithm is quick in prediction, and achieves multiple image tags better.

The invention relates to the field of machine learning, and specifically relates to a representative image selection method based on multi-example active learning, which comprises the following stepsof: (1) extracting original features of an image; (2) dimension reduction of original features; (3) carrying out original sample image pre-clustering by utilizing the dimensionality reduction characteristics; (4) selecting an initial training sample; (5) training a classifier; (6) adjusting the difficult-to-classify sample set; (7) adjusting the original sample set; (8) repeatedly executing the steps (5) to (7) to carry out iterative training until convergence; and (9) outputting a representative image. Screening out a sample set which contributes to the maximum classification precision of theclassifier from the original samples through pre-clustering, multi-example learning and active learning methods; and the samples are labeled for other machine learning tasks, so that the manpower consumed by labeling can be reduced, a part of noise samples can be filtered out, and the effective operation of other machine learning tasks is ensured.

The present invention discloses a human action classification method based on a video local feature dictionary. The method comprises a step of extracting a local feature from a training video with a category label, wherein a feature package is formed by the feature vector set in each segment of video, a step of grouping feature packages, using a multi-instance learning method to learn a local feature classifier, using a mode of cross-validation in the multi-instance learning, and marking multiple instances with largest ranking in each package as positive instances in updating the positive instances, a step of taking a learned classifier as the dictionary of feature encoding, and using a maximum pooling method to pool the local feature response to obtain the global vector expression of a video, and a step of using a global feature vector to learn, obtaining the classifier of each action category, and using the classifier to classify the action in a new video. According to the invention, the improvement of the accuracy of estimation is facilitated, the memory of an initial value by classification is avoided, and the accuracy of estimated positive samples is ensured at the same time.

The invention discloses a video abnormal behavior discrimination method based on non-local network deep learning, and belongs to the field of computer vision, intelligence and multimedia signal processing. A training set is constructed by using a multi-example learning idea, and positive and negative packages and examples of video data are defined and labeled. A non-local network is adopted for feature extraction of a video sample, an I3D network of a residual structure serves as a convolution filter for extracting space-time information, and a non-local network block fuses long-distance dependence information so as to meet the time sequence and space requirements of video feature extraction. After the features are obtained, a regression task is established through a weakly supervised learning method, and a model is trained. According to the method, unlabeled categories can be judged, and the method is suitable for the conditions that positive samples of abnormal detection tasks are rare and the diversity in the categories is high. The method meets the recall rate requirement of an abnormal scene and has engineering application value.

The invention discloses a neural network-based human body movement recognition method. The method includes the following steps of based on a video database, training N mutually independent 3D convolutional neural networks as a video feature extractor; according to the video feature extractor, training a multi-instance learning classifier; inputting a to-be-recognized video, extracting video features through the well trained network, and classifying movements by the classifier. According to the technical scheme of the invention, the influence of a large amount of noise features on a classification result is avoided. Meanwhile, the negative effect of a fixed sample length on a movement recognition result is also avoided.

The invention discloses a video facial expression early detection method based on multi-instance learning. The method comprises the steps that (1) video data of training samples and to-be-tested samples is preprocessed, and a facial region in each frame image in a video is extracted; (2) an LBP descriptor is adopted to perform feature extraction on the facial region, obtained through preprocessing, in each frame image, and feature vectors of each frame image are obtained; (3) an expression early detection function is solved by the adoption of an extended structured output support vector machine based on multi-instance learning according to the feature vectors of the training samples; and (4) the early detection function obtained in the step (3) is used to perform facial expression early detection according to the features vector of the to-be-tested samples in the step (2), and an expression early detection result is obtained. Through the method, expressions can be monitored in real time, and the recognition rate is high.

The invention discloses a target tracking method based on a partitioned multi-example learning algorithm. The method includes that a target image is divided into a plurality of partitioned image sheets; the weak classifier pool of each partitioned image sheet is obtained through the multi-example learning algorithm, and a plurality of weak classifiers having high classification capability are selected from the weak classifier pool to form strong classifiers; and during the target tracking, the score of an integrated classifier of the target image is calculated through the combination with the strong classifiers of all of the partitioned image sheets, and a target position is determined based on the score of the integrated classifier. The target tracking method based on the partitioned multi-example learning algorithm is high in tracking performance and stable in tracking process, and can solve the problems of serious lighting and pose change and shielding. The invention further discloses a target tracking system based on a partitioned multi-example learning algorithm.

The present invention is a video event detection method based on multimedia analysis, which includes: using text analysis to analyze the video to obtain a small amount of automatically marked video data; using multiple keywords to obtain a large number of event-related videos through a network video search engine Data; a semi-supervised multi-instance learning algorithm based on a graph model, using a small amount of automatically labeled video data and a large amount of event-related video data for training, and using the similarity criteria of event examples and the positive and negative attribute criteria of event packets to construct The mathematical description model of the event adopts a constrained convex-convex process method to solve a large amount of video data related to the event; the local similarity measure learning method uses the spatial distribution characteristics of the samples to learn and obtain effective similarity. The event model is used to realize event identification and location; according to the event model, the content of the video is semantically analyzed to obtain the location information of the event in the video.

The invention discloses a method for splitting and identifying character strings at complex interferences, which is characterized by comprising a learning phase and an identifying phase. The learning phase comprises the following steps of: splitting an image containing m characters into m pictures to form a multi-example learning packet, taking the same characters as a category, and classifying the packet and inputting to a base; and calculating an integrogram of the packet, extracting haar-like characteristics of the packet as an example of the packet, finding out key examples of each category by using a diversity density algorithm, and finally learning the key examples by using classified performances of an SVM (Support Vector Machine). The identifying phase comprises the following steps of: predicting the type of a new packet by using a learning result to identify the character strings. By using the method, the function of automatically identifying the character strings at the complex interferences can be realized, and the identifying speed and efficiency are higher.

The invention provides a multi-instance weighted packet learning method for online uncertain image recognition, which reduces the influence of noise data on classification results in online image recognition through improving a multi-instance learning algorithm, and puts forward the ideal of endowing different weights to packet instances, updating a classifier continuously and adjusting the weights to increase classification accuracy. The multi-instance weighted packet learning method is different from the traditional multi-instance learning method for image recognition in that: during the traditional multi-instance learning, a training set is composed of a plurality of packets, each packet comprises a plurality of instances, and tags of the packet instances are unknown. The multi-instance weighted packet learning method for online uncertain image recognition reduces the interference of noise instances on classification prediction through weighing the instances, iterates the training classifier, trains instance weights, represents the packets and achieves higher classification accuracy.

The invention discloses a weighted extreme learning machine video target tracking method based on weighted multi-example learning, solving the problem of bad tracking accuracy in the prior art. The method includes 1. initializing a Haar-like feature similar model pool and constructing a variety of feature model blocks, setting the weighted extreme learning machine network parameters; 2. extracting the training samples in the current frame and their feature blocks corresponding to the feature blocks of the different feature model blocks; 3. calculating the weighted multi-instance learning weight values; 4. constructing a plurality of networks corresponding to the different feature blocks and selecting the network with the largest similarity function value of the packet and the corresponding feature model block; 5. calculating the network global output weight values; 6. extracting the detection samples in the next frame and their corresponding feature blocks corresponding to the selected feature model blocks; 7. classifying the detection samples by means of the selected network and obtaining the target position of the next frame; and 8. repeating the above steps until the video is ended. According to the invention, the tracking accuracy is improved, and the target robustness tracking is realized.

The embodiment of the invention relates to an electrocardiosignal classification method and device, electronic equipment and a storage medium. The electrocardiosignal classification method disclosed by the embodiment of the invention comprises the following steps: acquiring electrocardiosignals; detecting a QRS complex wave from the electrocardiosignal; carrying out single heart beat cutting on the electrocardiosignals with the detected QRS composite waves and then packaging the electrocardiosignals into a plurality of heart beat packets; inputting the plurality of heart beat packets into a classifier model based on multi-example learning, obtaining a type identification result of each heart beat packet, wherein when the classifier model based on multi-example learning is used for classification, each heart beat packet is used as an example packet, each heart beat signal in the heart beat packet is used as an example in the example packet, and the type identification result comprises anormal rhythm type and an abnormal rhythm type. The electrocardiosignal classification method provided by the embodiment of the invention effectively reduces the time and labor cost spent in performing machine learning classification by manually labeling the heart beat.

The invention relates to a few-sample pedestrian re-identification method based on deep multi-example learning. The method comprises three stages: a network pre-training stage, a data set expansion stage and a network fine tuning stage. The method includes: after the pedestrian re-identification feature extraction sub-network is pre-trained, performing data expansion by utilizing a pedestrian keypoint feature region exchange algorithm; finely adjusting the pedestrian re-identification feature extraction sub-network and the feature aggregation sub-network by using the expanded data set; iteratively repeating data set expansion and network fine tuning until the feature extraction sub-network and the feature aggregation sub-network converge. Once the training is completed, the pedestrian re-identification model on the original domain is migrated and extended to the target domain by using few samples. On the premise of giving a small number of learning samples in the target domain, the pedestrian re-identification model can be effectively migrated and expanded to the target domain monitoring network, and the few-sample pedestrian re-identification method has the advantages of high accuracy, good robustness, good expansibility and mobility.