92 results about "Similarity learning" patented technology

The invention relates to a specific modal semantic space modeling-based cross-modal similarity learning method. The method comprises the following steps of: 1, establishing a cross-modal database which comprises data of multiple modal types, and dividing data in the database into a training set, a test set and a validation set; 2, aiming at each modal type in the cross-modal database, constructinga semantic space aiming at the specific modal, and projecting data of the other modal types to the semantic space so as to obtain a cross-modal similarity aiming at the specific modal; 3, fusing cross-modal similarities, aiming at specific modals, obtained from different modal semantic spaces to obtain a final cross-modal similarity; and 4, calculating a similarity between a query example and a query target by taking any modal type in the test set as a query modal and taking another modal type as a target modal, and obtaining a related result list of data of the target modal according to thesimilarity. According to the method, the correctness of cross-modal retrieval can be improved.

Methods and systems for improved license plate signature matching by similarity learning on synthetic images comprise generating a plurality of synthetic license plate images; applying one or more transformations to the synthetic license plate images to cause the synthetic license plate images to more closely resemble authentic license plate image captures; and providing the synthetic license plate images as inputs to a machine distance learning algorithm in which weighted similarity scores are calculated between signatures of analogous and non-analogous license plate images and one or more sets of signature weights are iteratively adjusted to increase the likelihood that comparing analogous license plate images results in high weighted signature similarity scores and comparing non-analogous license plate images results in low weighted signature similarity scores.

The invention discloses a feature bag image retrieval method based on a Hash binary code. The method comprises steps that, a vision term list is established; tf-idf (term frequency-inverse document frequency index) weight quantification of vision terms is carried out; vision term characteristic quantification of an image is carried out; an inverted index is established; a feature binary code projection direction is learned; feature binary code quantification is carried out; candidate image sets are retrieved. According to the method, the index is established for an image database, rapid image retrieval is realized, and retrieval efficiency is improved, moreover, through a binary code learning method having the similarity retention capability, the binary code is learned from spatial distance similarity and meaning distance similarity as signature, and image retrieval accuracy is improved. The feature bag image retrieval technology based on the Hash binary code has properties of high efficiency and accuracy, and relatively high use values are realized.

The invention discloses a deep learning based video retrieval method which mainly comprises the following parts: performing video pre-processing by using a convolutional neural network; extracting feature vectors of the video after pre-processing by using a long short-term memory; and finally learning by a similarity learning algorithm to obtain a distance calculation method, and performing similarity calculation according to the method and ranking, so as to obtain a video retrieval result. According to the method disclosed by the invention, scene segmentation and key frame selection are performed by the convolutional neural network, and high-level semantics representing video is extracted, so as to acquire a proper quantity of key frame sequences, and effectively avoid false detection andmissing inspection in shot segmentation. According to method disclosed by the invention, time-order characteristics of the video are extracted by the long short-term memory, so as to obtain a more accurate retrieval result. By similarity learning and a text based matching method, matching accuracy of a similarity measurement method can be promoted. By adopting the method disclosed by the invention, accurate retrieval on a large scale of videos can be realized.

The invention discloses a human face recognition method and device based on tensor description. Firstly, similarity learning is conducted on image samples with labels and samples to be classified and without labels, and similar adjacent figures and normalized weights are configured to represent sample similarity; secondly, a category label matrix is initialized manually; thirdly, to effectively achieve direct induction of human face images outside samples, a regularization term capable of conducting direct induction on the images outside the samples and based on the tensor description is integrated into an existing label propagation model; finally, system modeling is finished through influences of parameter weighing similarity measuring, initial category labels and the regularization term based on the matrix pattern on the human face recognition; the maximum value of the probability of similarity in system output is taken to be used for conducting category identification of the human face images, and the most accurate system recognition result is obtained. By introducing the concept of tensor description, the topological structures among image pixels can be effectively maintained in the induction process of the human face images outside the samples, and the system expansibility is good.

The invention discloses a nuclear norm regularization based low-rank image characteristic extraction identification method and system. Firstly an original training image is subjected to similarity learning to construct a reconstruction weight coefficient; and secondly a nuclear norm measurement based neighborhood reconstruction error is minimized and a projection matrix is subjected to nuclear norm regularization processing to obtain a low-rank projection matrix capable of directly extracting two-dimensional image characteristics, so that the topological structure and correlativity among image pixels can be effectively kept. In addition, it can be ensured that low-rank salient image characteristics are obtained by optimization. An original test image is directly embedded in the low-rank projection matrix obtained by training, low-rank salient characteristics of the image are output, classification is performed by utilizing a nearest neighbor classifier based on low-rank salient characteristics in a training set, and category labels of training image samples with highest characteristic similarity with test image samples are obtained, thereby finishing the classification of the test image samples. By introducing nuclear norm regularization, the robustness of noises in a characteristic extraction process can be effectively ensured and the system performance is better.

The invention discloses a new cell type identification method based on similarity learning and enhancement thereof. The method designs a new global similarity calculation method, combines with other three conventional local similarity information, screens the genes, and carries out enhancement processing on the global similarity with sparse properties. According to the method, a global similaritycalculation method different from the traditional calculation of the local point-to-point similarity is used, the gene selection and the similarity enhancement are carried out by combining multiple different similarities including the global similarity and the local similarity, and a similarity matrix rich in information is obtained. According to the method, the influence of the factors, such as the technical noise, the biological noise, etc., carried by the single cell data can be effectively reduced, and the type of the single cell can be more accurately identified.

The invention provides a thesis recommendation method based on a multilayer drawing, which comprises the following steps: Step I, taking a target thesis and candidate theses as nodes in the drawing, constructing a thesis drawing according to reference relationships among the target thesis and the candidate theses and among the candidate theses; Step II, calculating weights of words in the theses by a word weight calculation method, removing the words with the weights less than a threshold, taking the rest words as the nodes, constructing a keyword feature map by virtue of semantic dictionary information; Step III, formulating a target equation by a similarity learning method based on the multilayer drawing; and Step V, optimizing a target function by a heuristic search algorithm of the map, updating weights of sides in the multilayer drawing, and recommending the thesis according to a final multilayer drawing result. The method allows a computer to be capable of recommending the target thesis according to the interested theses read or marked by a user.

The invention provides an image matting method and system. The image matting method comprises the steps of 1, training an image matting model by using an image training set; wherein samples in the image training set comprise an original image, and a tripartite graph, a gold standard mask and a gold standard combined mask which correspond to the original image; wherein the image matting model comprises a depth feature extraction module which is used for learning semantic features and detail information features of an original image; The similarity learning module is used for fusing the semanticfeatures and the detail information features to obtain a similarity relation of the pixel points; The mask propagation module is used for obtaining a mask value of each pixel point through a propagation algorithm according to the similarity relation between the trisection image and the pixel points, and outputting an alpha mask image corresponding to the original image; and 2, inputting the original image to be subjected to image matting and the corresponding tripartite image into the trained image matting model, and outputting an alpha mask image corresponding to the original image to be subjected to image matting.

An object identification apparatus selects, for each partial area of an object in a registered image, a corresponding sample image from sample images based on feature quantities of the partial area, for objects in the registered image, sets a similarity of feature quantities for each partial area between objects of an identical individual and between objects of different individuals based on a similarity related to the selected sample images for each partial area, makes a discriminator learn based on the set similarity, acquires a similarity for each partial area between objects in an input image and the registered image, and determines whether the object in the input image is identical to the object in the registered image based on the acquired similarity and a result of discrimination by the discriminator.

The invention discloses a human face set matching method and system based on similarity learning. Training set samples and testing set samples are selected from original data samples, training samples are selected, the true similarity is calculated and is compared with the worked out calculation similarity, and then a final classifier is selected; the geometric mean of each type of training samples and all of testing samples in the testing set samples are substituted into the final classifier, classifying results are obtained, and then the categories of the testing samples are obtained. According to the scheme, part of samples are selected as the training samples to carry out training, classifiers are selected, and therefore it is avoided that all samples serve as the training samples to be trained, the training process is simplified, the complex process is avoided, and the training speed is improved. In addition, according to the scheme, the geometric mean of each type of samples in the training set samples is selected to construct a plurality of different classifiers, and the effect of the accurate result through the simple operating process is achieved.

The invention provides an image segmentation method based on correlation matrix self-learning and an explicit rank constraint. Low rank representation, similarity learning and a cluster structure constraint are incorporated into the same frame and are applied to medical CT image segmentation. The method comprises the steps that (1) a CT original image is preprocessed; the gray scale is used to correct and enhance the image; histogram equalization is carried out; and a median filter method is used to filter a target area to reduce interference and noise in the image; (2) an algorithm based on correlation matrix self-learning and the explicit rank constraint is used to solve the eigenvalue projection matrix of the image; and (3) a traditional clustering algorithm is used to cluster and split a projection matrix, and a region of interest is calibrated.

The invention provides an image similarity measurement method based on kernel preserving. The method comprises the steps that firstly a loss function is defined, then the loss function is simplified by defining a kernel function, a regularization function is added into the loss function to obtain a complete objective function, finally, the objective function is optimized and solved to obtain a final reconstruction transformation matrix, and two indexes including the accuracy rate and the regularization mutual information amount are adopted to measure the performance of the final reconstructiontransformation matrix. According to the method, the kernel function is defined to minimize a reconstruction error, and the similarity learning is carried out on original image data samples, so that better global relationships among the image data samples are reserved, and based on the similarity information, more accurate clustering is carried out on the images by using a spectral clustering algorithm. The method has universality and can be used for clustering, classifying, recommending systems and other problems, an effective basic module is provided for a method based on similarity learning, and meanwhile great potential is achieved in the application of image mapping to low dimension.

The invention discloses a deep learning remote sensing image registration method based on sub-image matching and application. The method comprises a sub-image matching stage and a transformation parameter estimation stage which are completed by the convolutional neural network. Comprising the following steps: 1, cutting a series of sub-images containing a plurality of features from an image; extracting sub-image features through a sub-image similarity learning network ScoreCNN with a feature vector inner product structure; estimating the similarity of the sub-images in the fusion stage; and according to the similarity, searching matched sub-images with high confidence by using a rapid screening algorithm; and 2, inputting the matched sub-images to the corresponding coordinates in the original image into a transformation parameter estimation network ETPN with a weight structure and position codes, and outputting a transformation matrix between the images to be registered. According to the invention, the problem of algorithm failure caused by insufficient correctly matched features in image registration with large feature change in a traditional registration frame is solved, and meanwhile, the precision of the deep learning registration method based on parameter regression is improved.

The invention discloses a robust measurement based handwriting identification method and system. The method comprises the steps of: constructing a weighted similar map by performing similarity learning on a handwriting training sample; and keeping local features of all training samples while compacting a local intra-class divergence and separating a local inter-class divergence. In order to improve robustness of handwriting description, 1-norm measurement is proposed to be applied in a semi-supervised learning model, so as to design a performance robustness handwriting identification method and system, and output a projection matrix P that can be used for handwriting image feature extraction within a sample and outside the sample. Induction on images other than the sample comprises the steps of: projecting a test sample to the projection matrix P to input an extracted feature into an effective label propagation classifier for classification; and selecting a place of maximum probability in a corresponding category of soft label to determine a category of the test sample, so as to obtain a most accurate character recognition result. Meanwhile, by establishing a ratio model, model parameters are reduced, and the projection matrix P meets the orthogonal property.

A system and method for distributed similarity learning for high-dimensional image features are described. A set of data features is accessed. Subspaces from a space formed by the set of data features are determined using a set of projection matrices. Each subspace has a dimension lower than a dimension of the set of data features. Similarity functions are computed for the subspaces. Each similarity function is based on the dimension of the corresponding subspace. A linear combination of the similarity functions is performed to determine a similarity function for the set of data features.