294 results about "Hash coding" patented technology

E-mail addresses are converted by a hashing function into hash codes. An e-mail mass mailer that can have millions of addresses hash codes all addresses to which a message might be sent to form a first list. An entity that wishes to exclude delivery of a message to various e-mail addresses, hash codes such addresses to form a second list. Hash codes in the first list that are also in the second list are purged from the first list to yield a third list of hash codes, each of which represents an e-mail address to which a message may be sent.

The invention discloses a rapid image retrieval method based on supervised topology keeping hash. The method includes the steps of S1, extracting features of obtained training images and inquiry images, converting feature spaces into new nuclear spaces, and obtaining nuclear space representation of each image; S3, conducting binary coding on the training images and the inquiry images; S4, conducting image retrieval through binary codes. For solving the rapid image retrieval problem, hash coding is studied in the nuclear space with higher expression capacity, supervise information and topology keeping information are added in the hash mapping matrix studying process, a studied mapping matrix has higher semantic expression capacity and higher within-cluster variation expression capacity, and therefore the studied binary codes are more suitable for image retrieval tasks, retrieval accuracy is improved, and retrieval result sequencing is optimized.

The invention discloses a cross-modal hash retrieval method and system for fusing supervision information. The method comprises the following steps: constructing an image network, a text network and afusion network; The training sample pairs of image and text features are obtained and input into image network and text network respectively. Taking the output characteristics of the image network and the text network as the inputs of the fusion network, and defining the output of the fusion network; Constructing an objective function for learning a unified hash code according to the output of the converged network and the similarity between pairs; Solving the objective function to obtain a unified hash code; The unified hash code is used as the supervisory information and the semantic information is combined to train the hash network of a specific mode. The invention simultaneously learns the feature representation and the hash coding based on the end-to-end depth learning framework, canmore effectively capture the correlation between different modal data, and is conducive to the improvement of the cross-modal retrieval accuracy.

Hash numbers are assigned to each individual ballot choice made by a particular voter and a hash total, establishing the voter's set of ballot choices, is recorded and a printed receipt is issued to the voter, bearing the voter's identification number, to assure him that his vote was correctly recorded along with his hash total. If the voter presents his receipt, to deny that he voted as shown on the receipt, an unfavorable comparison of the previously recorded hash total, with the hash total on the receipt, proves that the receipt was forged, and each such tampering attempt and voter ID is recorded. Recounts are unnecessary, unless the number of such recorded attempts at tampering exceed a predetermined number. A hacker attempt to change a recorded vote will fail because the hash totals of the altered ballot won't match the correct hash total because the hash encoding algorithm is secret.

The invention discloses a cross-model Hash retrieval method on the basis of cooperative training and matrix decomposition. By the aid of the cross-model Hash retrieval method, the similarity between models and the internal similarity of the models can be effectively constrained for unlabeled cross-model data. The cross-model Hash retrieval method includes implementation steps of acquiring original data and carrying out normalization processing on the original data; carrying out cooperative training to obtain constraints between the models; acquiring internal constraints of the models by the aid of neighbor relations; decomposing training data matrixes and adding the constraints between the models and the internal constraints of the models into the training data matrixes to obtain objective functions; carrying out alternate iteration to obtain expressions of basis matrixes, coefficient matrixes and projection matrixes; carrying out quantization to obtain Hash codes of training data sets and test data sets; computing the Hamming distances between every two Hash codes of the data sets; sorting the Hamming distances to obtain retrieval results. The cross-model Hash retrieval method has the advantages that constraints on the similarity between the models of the cross-model data can be obtained by the aid of cooperative training processes, accordingly, the image and text mutual retrieval performance can be improved, and the cross-model Hash retrieval method can be used for picture and text mutual search service of mobile equipment, internets of things and electronic commerce.

The invention provides a large-scale image library retrieval method based on the local similarity hash algorithm. The large-scale image library retrieval method includes the steps that a part of images are selected from an image library to be retrieved to serve as a training image set, and SIFT features of training images are extracted; a K means algorithm is used for conducting clustering on the SIFT features of the training image set to obtain a codebook; the inverse frequency of each code word in the codebook is calculated on the training image set; local sensitive hash coding is conducted on each code word; SIFT features of a queried image and images in the image library to be retrieved are extracted respectively; for each image, the word frequency of each code word in the corresponding image is calculated, and then the weight of each code word is obtained; local similarity hash codes of the images are calculated by using the similarity hash algorithm; the Hamming distances between a hash code of the queried image and the hash codes of the images to be retrieved are calculated; the Hamming distances are used for retrieving the images similar to the queried image rapidly. The large-scale image library retrieval method has good universality, reduces data storage space and also improves the query retrieval efficiency.

The invention relates to a network training method for video data on the basis of deep learning and hash coding. The method comprises the following steps: extracting a feature matrix of a video sample by using a deep neural network; carrying out modeling by using the acquired feature matrix of the video sample as a whole body to obtain high-dimensional real value representation of the video sample; and further representing the obtained high-dimensional real value representation into binary hash coding by using a deep network.

The invention relates to an RDF data storage and query method combined with star figure coding. The RDF data storage and query method comprises the steps that S1, RDF data are preprocessed, and the RDF data are presented in an RDF data map mode; S2, an input SPARQL query statement is presented in an SPARQL query graph mode, and query decomposition is carried out; S3, the SPARQL query statement is preprocessed, and the task number of whole query, the connecting sequence of query star sub-nodes and relevant information of the query star sub-nodes are obtained; S4, the SPARQL query statement is executed, query connection planning is carried out, a Map Reduce parallel computation frame of Hadoop is adopted, and the number of times of starting a query task Job is decided according to the relevance of the SPARQL query statement; S5, subgraph query is carried out, and a Map function is adopted; S6, a result connecting algorithm is carried out, and a Reduce function is adopted. Due to the fact that a Hash coding index query strategy based on star configuration is adopted, stored data redundancy and the number of query tasks are reduced, and query efficiency is improved.

The invention discloses a content retrieval method for a chrysanthemum image based on deep hash learning. A deep neural network algorithm and Hash coding are used to recognize and retrieve images. Themethod comprises: firstly, establishing a training set and a test set to perform pre-data-processing on a to-be-identified image, to enhance generalization ability and recognition degree of the image; secondly, establishing a chrysanthemum image feature extraction model through a convolution neural network, and realizing query calculation by hash coding in a hash layer of the convolution neural network. The method is based on the deep hash learning, so that when high-dimensional data is mapped to a low-dimensional space, similarity of the data in high-dimensional space can be maintained in aHamming space, a coding balance criterion is maintained when the high-dimensional data is mapped to the low-dimensional space. The method processes a chrysanthemum data set, enhances the data set to enhance model generalization ability, and improves image retrieval quality and retrieval efficiency.

The embodiment of the invention discloses an image retrieval method, device and apparatus and a computer readable storage medium. The method comprises the steps of enabling image pairs in an image database to serve as input, the distance between Hash coding pairs obtained through mapping of the image pairs, enabling the label category and the feature similarity of the image pairs to serve as lossvalues, and adopting a machine learning optimization algorithm to optimize the loss values so as to obtain a deep Hash mapping model through training; mapping the to-be-retrieved image into a to-be-retrieved Hash code by using a deep Hash mapping model; And searching a pre-constructed Hash code library for a target image whose Hamming distance difference with the to-be-retrieved Hash code satisfies a preset condition, outputting the target image as a retrieval result of the to-be-retrieved image in the image database, and mapping each image in the image database through a deep Hash mapping model to obtain the Hash code library. According to the image retrieval method and device, the problem that Hash codes of the same category of images are too consistent in the related art is effectivelysolved, and therefore accurate retrieval of the same category of images is achieved.

The invention discloses a sketch retrieval method based on content adaptive Hash encoding. The method is characterized by including the steps: candidate windows are adaptively selected from sketches or outlines under retrieval according to their contents and are used for feature extraction, and information included in whole pictures are evenly distributed to the windows; significance of each feature window is detected by a key point based significance detection method; local visual features, significance and structural spatial features of the stretches or outlines are combined and complied into Hash feature codes by a locality sensitivity based Hash algorithm; the Hash feature codes of the sketches or outlines are indexed, Hamming distances of the Hash feature codes are calculated to measure similarity of the sketches, and highly similar ones of the sketches are returned to users. The method has the advantages that precision is higher, applicable range is wider and matching capacity is higher.

The invention discloses an image Hash coding method based on deep learning. The method includes following steps: step 1, adopting an image classification model GoogLeNet trained at an ImageNet image recognition database as an initialized basic network structure, and replacing the final classification layer of the GoogLeNet model by a Hash layer, wherein the unit number of the Hash layer is the bit number to be coded by images; step 2, optimizing parameters of the GoogLeNet model; and step 3, inputting images in an image retrieval data set to the optimized GoogLeNet model, and quantifying the floating point number output by the GoogLeNet model to binary codes to obtain the binary code of each image. According to method, combined optimization of image characteristics and a Hash function is realized, and the defect that the Hash codes obtained by learning of the conventional Hash method is not in accordance with the image characteristics is overcome.

The invention discloses a method for detecting an approximately repeated image. The method comprises: step one, symmetric hierarchy hash coding network feature extraction and description are carried out on to-be-matched images to obtain image features for constructing a locality sensitive hash index structure; step two, on the basis of the image features obtained at the step one, a locality sensitive hash index structure with a balanced load is constructed; and step three, a symmetric convolution neural network feature extraction is carried out on an inquired image and a corresponding hash code of the inquired image is calculated, correlation of the to-be-matched images are ranked based on the locality sensitive hash index structure with the balanced load constructed at the step two, and an image with the highest correlation is selected to be an approximately repeated image. Therefore, approximately repeated image detection can be carried out rapidly and effectively.

The invention relates to a model training method based on deep learning and hash coding. The method comprises the steps of regarding partially labeled image data as training data of a network model, and representing the training data as an analog-two-value hash code through a deep network, wherein the analogy-two-value hash code refers to a simulation two-value hash code of which the value is a continuous value; regarding the obtained analog-two-value hash code as input, connecting the analog-two-value hash code to one or more task layers, and using one or more tasks to conduct training at the same time; obtaining a two-value hash code which is used for representing the training data and carries feature information capable of being retrieved based on the analog-two-value hash code.

A computer implemented method for indexing multimedia vectors and for searching and retrieving a query vector using a locality sensitive hashing. Indexing is performed by calculating hash codes from the multimedia vectors using several hash functions. Each hash code is a different subset of the entries in the hash vector. The method utilizes the structure of the hash vector space in order to define the hash codes in a way that improves the retrieval efficiency. Retrieval is performed by applying the hash functions to a query vector and measuring the distances between the query vector and multimedia vectors with hash codes identical to the hash codes of the query vector.

The invention discloses an attention mechanism and hash-based image retrieval method and device and a storage medium, and the method comprises the steps: obtaining the discrete hash code of each imagebased on a pre-trained attention mechanism-based hash feature extraction model for each image in an original data set, and building an image feature library; obtaining a discrete hash code of a to-be-detected image based on a pre-trained hash feature extraction model based on the attention mechanism; and querying data closest to the discrete hash code Hamming distance of the to-be-detected imagein an image feature library, wherein the image corresponding to the data is a retrieval result. The Hash feature extraction model based on the attention mechanism gives full play to the ability of thedeep convolutional neural network to extract the abstract semantic features of the image; and by embedding the attention module, the network can focus on the visual content capable of expressing semantic information of the whole image as much as possible in the image, the expression effect of hash coding is improved, and image retrieval is more accurate and faster.

The invention discloses a target tracking method based on triple twin hash network learning, and relates to the technical field of computer vision, target tracking and deep learning. According to themethod, firstly, a triple twin hash network is constructed, and the network is composed of a data input layer, a convolution feature extraction layer and a hash coding layer. In the initial training process of the network, a training data set and a random gradient descent back propagation algorithm are used for training the triple twinning Hash network, and after training is completed, the initialcapacity of target positioning can be obtained through the network. In the tracking process, firstly, an input image passes through a triple twin region recommendation network to obtain correspondingcandidate frames, then the candidate frames are input into a triple twin Hash network to be subjected to forward processing, the similarity between each candidate frame and a query sample is calculated, the candidate frame with the highest similarity is selected as a tracking target object, and therefore target tracking is achieved.

The invention discloses an ultra low complexity image retrieval method based on sequence preserving hashing, and relates to image retrieval. For images in an image library, a part of images are selected randomly to serve as a training set, and corresponding image features are extracted; the dimension of an original image feature is reduced to the length same as a hash code through a nonlinear principal component analysis method; a series of supporting points are obtained through a K-means clustering algorithm to serve as the basis of follow-up hash function learning; a corresponding hash function is learned through iterative optimization; the corresponding hash function is output, and a hash code of the whole image library is calculated; for a querying image, a corresponding GIST feature is extracted, hash coding is conducted on the image feature according to the hash code function obtained through training, the hamming distance between the hash code of the querying image and an image feature code in the image library is calculated, the similarity between the querying image and a to-be-retrieved image in the image library is measured through the hamming distance, and the image high in similarity is returned.

The invention relates to an image retrieval method for a multi-index disk Hash structure. The method comprises the following steps that in an offline index stage, high-dimensional features are extracted from media data; a Hash mapping method is adopted for mapping the high-dimensional features into Hash codes; the Hash codes are averagely partitioned into code substrings; the code substrings are inserted into corresponding B+ tree index structures based on a disk respectively; the steps are repeated m times, and the multi-index disk Hash structure is formed. In an on-line inquiry stage, the inquired media data is subjected to feature extraction, Hash coding and code partitioning, then the code substrings are obtained, r neighbor results of the codes in disk indexes are searched and combined till a k neighbor result is found, and the steps return. The advantages of the multi-index Hash method and the advantages of a B+ tree storage structure are combined, the retrieval accuracy and retrieval speed of the index structure are improved, and meanwhile the supported data volume is increased.

The invention provides a depth neural network training method and device based on a Hash coding layer. The method comprises the steps: obtaining a pre-trained neural network model which comprises a basic feature layer; Inserting a first full connection layer for Hash coding linear mapping and the Hash coding layer serving as an activation function behind the basic feature layer in the neural network model; inserting a second full connection layer serving as an output layer behind the Hash coding layer; and carrying out the training of the weight of the first full connection layer and the second full connection layer. According to the invention, the method takes the Hash coding layer as the activation function of depth neural network training, carries out the training of the neural network through employing a method of random gradient descent, achieves overall optimization, achieves the direct training of a model outputting a binary code, and does not need to relax the optimization conditions.

The invention provides a method for rapidly filtering a key word. The method comprises the steps of: indexing the key word, and matching the key word. The method comprises the steps of: specifically carrying out hash encoding on each key word in a key word set, and carrying out key word matching by hash encoding, wherein the efficiency is higher than that by using a common multi-mode key word matching algorithm. An end mark is adopted to trigger key word matching, so as to achieve the rapid judgment that a current text can not be matched with any key word in the text scanning process; and the key word is matched through a hash value only when head and tail characters are consistent. Thus, the text scanning and matching speeds are greatly quickened.

The invention discloses an image classification and recognition method based on a twin network. According to the method, repeated inspection is carried out through Hash coding; preprocessing such as boundary frame prediction and affine transformation is simplified, and the data set quality is improved; Then, the test set and the training set are traversed through Hash coding, matched picture pairsand unmatched picture pairs are formed through combination in sequence, the matched picture pairs and the unmatched picture pairs are alternately input into a twin classification network for trainingfitting, and finally the classification effect that pictures of the same type are classified into the same type and different types can be effectively distinguished is achieved. According to the method, the defect of low prediction accuracy of an early-stage deep learning classification method when the test set is more than the training set and the category data is unbalanced is overcome, and theproblems that the classification data is unbalanced, the test set is more than the training set and the overall scale is small in an actual scene are solved. Besides, by encoding the picture data, the matching picture pair and the mismatching picture pair are analyzed, so that the accuracy of the twin classification network is improved, and a good example is provided for picture classification inan actual scene.

The invention discloses a book recommending method based on multi-view hash, comprising the following steps of 1) screening behavior data of a user on two views from a log collection system, wherein the behavior data comprise book clicking data and search data; 2) establishing user feature vectors of the user for clicking and searching the views; 3) obtaining a user hash code, a hash function and weights of the two views through a multi-view hash algorithm by utilization of the behavior data of the two views; 4) searching a similar user for a target user by utilization of the obtained user hash code; 5) obtaining a book set clicked by the similar user, taking the book set as a recommended candidate list, calculating the book preference degree of the target user, and returning front N books with maximal preference degree of the target user. According to the book recommending method based on the multi-view hash, on one hand, the behavior data of the user on two views can be integrated into the hash code, so that the book recommendation accuracy is improved; on the other hand, the Hamming distance calculation speed of the hash code is very fast, so that the book recommendation efficiency can be improved.

The invention discloses a large-scale image library retrieval method based on a self-adaptive bit allocation Hash algorithm. The method comprises the following steps: selecting a part of images as a training set from an image library to be retrieved, and extracting a GIST characteristic of the training set; projecting the characteristic data of the training set by using principal component analysis (PCA), and calculating the dispersion of each dimension of training data; according to the dispersion of different dimensions, allocating different bits to encode the data in a self-adaptive manner; obtaining a sub-code according to the code length of each dimension and each dimension of a threshold code, and splicing complete codes of the data in pair; corresponding to the processing and training process of a checked image and the characteristic data in the image library to be retrieved, respectively calculating Hash codes of the image to be retrieved and the characteristics of the checked images; calculating the Hamming distance of the Hash codes, thereby rapidly retrieving similar images. The method is high in universality, the neighbor structure of original characteristic data can be well maintained, and as the data are encoded by using a Hash method, the storage space of data is reduced, and the retrieval efficiency in checking is improved.

The invention provides an image retrieval method based on multi-feature fusion. The image retrieval method is used for solving the problem that an image retrieval method based on a single feature cannot meet the query requirement of a user. The method comprises the steps of performing noise reduction processing on a to-be-retrieved image by utilizing a filtering method; performing feature quantification by utilizing the improved HSV color space to extract global features of the to-be-retrieved image; performing multi-scale morphological gradient extraction on the denoised image to extract local features of the to-be-retrieved image; performing adaptive fusion on the global features and the local features to obtain an adaptive fusion image; carrying out hash coding on the self-adaptive fusion image, calculating the similarity between the to-be-retrieved image and all the images in the database through Hash codes, and selecting the first several images with the highest similarity with the to-be-retrieved image as retrieval results of the to-be-retrieved image. According to the method, the feature points of the image are fully extracted, and the edge information of the image is protected more comprehensively in the local feature extraction process, so that the retrieval accuracy is improved, and the retrieval time is shortened.

The invention provides a multi-label image retrieval method fusing triple loss and generative adversarial network, which comprises the following steps of: establishing a deep learning framework, and deploying a generative adversarial network model; inputting the image data set into the generative adversarial network model to obtain a multi-label image and triple data; constructing a triple loss function based on the multi-label image; selecting a first image from the image data set to train the deep hash coding network so as to obtain a trained deep hash coding network; selecting a preset number of second images from the image data set, and inputting the second images into the trained deep hash coding network to obtain a hash vector database; and inputting a first image to be retrieved into the trained deep hash coding network to retrieve a second image similar to the first image. According to the invention, the generative adversarial network is used to generate the multi-label generation picture similar to the data set sample, the training data volume is expanded, and the retrieval speed and precision of the image are improved.

The invention discloses a remote sensing image content retrieval method for semi-supervised deep adversarial self-coding Hash learning. The method comprises the steps of establishing a remote sensingimage feature library, and selecting a plurality of samples as training samples; training an adversarial self-coding Hash learning model by using the training sample; performing Hash coding on the whole remote sensing image feature library by using an adversarial self-coding Hash coding model to obtain a Hash database; processing a query image input by a user, obtaining a feature vector corresponding to the query image through the same pre-training network, and performing Hash coding by using a confrontation self-coding Hash learning model to obtain a corresponding Hash code; and finally, calculating similar distances between the query image and all images in the image library, returning the images required by the user according to the distances from small to large, and finding the corresponding image in the remote sensing image library according to the index to complete image retrieval. According to the method, high retrieval precision can be kept under semi-supervised learning, Hashcoding is more efficient, smaller quantization loss is achieved, and the retrieval precision is further improved.

The invention discloses a blocking perception Hash tracking method with shadow removing. The method comprises the steps of: determining shadow areas in an image according to the distribution characteristics of a shadow image in each channel grey-scale map of a CIELAB color space; then utilizing a color constancy theory to recover pixel points in the shadow areas to a non-shadow effect; combining blocking perception Hash coding values with color self-similarity for forming a similarity measure, and carrying out matching on tracked target sub-blocks of adjacent frames based on the similarity measure; and finally, combining the above sub-blocks to obtain the regional position of the tracked target in the current frame, and realizing the tracking of the tracked target in a video. The blocking perception Hash tracking method has the advantages that according to different moving ranges and deforming degrees of human body parts, a human body target is divided into eight sub-blocks, and on this basis, and the blocking perception Hash coding method is provided to solve the problem of an existing tracking algorithm that the tracking is unsuccessful when the human body is partially or totally shielded or partially rotated and when the illumination in the shadow areas and non-shadow areas of a natural scene changes suddenly.

A massive image library retrieving method based on an optimal K mean value Hash algorithm comprises the steps that part of images are selected from an image library to be retrieved to serve as a training image set, and firstly GIST characteristics of images of the training image set are extracted; characteristic value allocation preprocessing is conducted on characteristic data of the training image set; the preprocessed characteristic data are divided into a plurality of sub-spaces; a codebook and codes of the codebook of the corresponding sub-space are trained out for each sub-space; the processing and training process of characteristic data in the image library to be retrieved corresponds to the processing and training process of characteristic data in inquiring images, the GIST characteristics of images to be retrieved and the GIST characteristics of the inquiring images are extracted respectively, then Hash codes of the characteristics of the images to be retrieved and Hash codes of the characteristics of the inquiring images are calculated, the Hamming distance between the codes of the characteristics of the images to be retrieved and the codes of the characteristics of the inquiring images is calculated, and thus similar images are fast retrieved. The massive image library retrieving method based on the optimal K mean value Hash algorithm has good universality, the storage space for data is reduced, and the inquiring retrieving efficiency is improved.