61 results about "Hamming space" patented technology

The invention discloses a sparse dimension reduction-based spectral hash indexing method, which comprises the following steps: 1) extracting image low-level features of an original image by using an SIFT method; 2) clustering the image low-level features by using a K-means method, and using each cluster center as a sight word; 3) reducing the dimensions of the vectors the sight words by using a sparse component analysis method directly and making the vectors sparse; 4) resolving an Euclidean-to-Hamming space mapping function by using the characteristic equation and characteristic roots of a weighted Laplace-Beltrami operator so as to obtain a low-dimension Hamming space vector; and 5) for an image to be searched, the Hamming distance between the image to be searched and the original image in the low-dimensional Hamming space and using the Hamming distance as the image similarity computation result. In the invention, the sparse dimension reduction mode instead of a spectral has principle component analysis dimension reduction mode is adopted, so the interpretability of the result is improved; and the searching problem of the Euclidean space is mapped into the Hamming space, and the search efficiency is improved.

A system comprises an encoder, a precoder, a PRML channel, a detector, and a decoder. An input signal is received by the encoder. The encoder generates a code string by adding one or more bits and outputs the code string to the precoder. The encoder applies encoding such that the code string after passing through the precoder has a Hamming distance greater than one to eliminate error events with a small distance at the output of the PRML channel. The present invention also provides codes that after precoding have Hamming distance of 2 and 0 mod 3 number of ones. These codes when used over a PRML channel in an interleaved manner preclude + / -( . . . 010-10 . . . ) error events and error events + / -( . . . 01000-10 . . . ). The code string also has a predetermined minimum number of ones at the output of the PRML channel to help derive a clock from the input signal. The encoder provides a "systematic" encoding scheme in which for many code strings the encoded bits are the same as the input bits used to generate the encoded bits. This systematic approach of the present invention provides an encoder that is easy to implement because a majority of the bits directly "feed through" and non-trivial logic circuits are only needed to generate the control bits. The systematic encoding also dictates a decoder that is likewise easy to construct and can be implemented in a circuit that simply discards the control bit. The encoder preferably comprises a serial-to-parallel converter, a code generator, and a parallel-to-serial converter. The code generator produces a rate 16 / 18 or 16 / 17 code. The present invention also includes a method that is directed to encoding bit strings and comprises the steps of: 1) converting the input strings to input bits, and 2) adding at least one bit to produce an encoded string with many ones and a Hamming distance greater than one after precoding.

The invention discloses a cross-media Hash index method based on a coupling differential dictionary. The cross-media Hash index method based on the coupling differential dictionary comprises the following steps that (1) modeling is conducted on the correlation of a plurality of modal data based on a graph structure, the similarity inside a modal is determined through the Euclidean distance between the data low-level features, the correlation between modals is determined by using the known correlation of different modal data, and classification label information of the data is used for improving the differentiation of the data on the graph structure; (2) the differential coupling dictionary is studied on the correlation of the data on the graph structure obtained in the step (1); (3) sparse coding is conducted on the different modal data by using the studied coupling dictionary in the step (2) and mapped inside unified dictionary space; (4) a Hash mapping function from the dictionary space to binary hamming space is studied. The cross-media Hash index method based on the coupling differential dictionary can realize the efficient cross-media searching of mass data based on content, and a user can submit a searching example of one modal to search a media object of another modal.

A migration retrieval method based on a semi-supervised countermeasure generation network is provided. A countermeasure generation network is designed to retrieve hashes across data domains, and the goal is to map the original and target datasets into a common Hamming space, so that the image retrieval in a particular scene can be migrated to a retrieval image of another scene through the learningof the semi-supervised antagonism generation network. Therefore, the problem that the unlabeled data can not be fully utilized and the retrieval model is only suitable for a single scene in the era of big data is solved. The invention effectively improves the automatic and intelligent level of image retrieval.

The invention discloses a binary code dictionary tree-based search method. The method comprises the steps of obtaining a binary code of each image in a database, and dividing each binary code into m sections of substrings; for the jth sections of the substrings of all images in the database, establishing a binary code dictionary tree of the jth sections of the substrings, wherein the number of binary code dictionary trees is m, and each binary code dictionary tree comprises internal nodes and external nodes; obtaining the binary code of the to-be-queried image and the m sections of the substrings of the binary code; for the jth section of the substring of the binary code of the to-be-queried image, searching for the binary code with a Hamming distance not exceeding a value defined in the specification in the binary code dictionary tree corresponding to the jth sections of the substrings of all the images in the database; and traversing all the substrings of the binary code of the to-be-queried image to obtain a query result of each substring, wherein j is smaller than or equal to m. According to the method, the search quantity can be reduced during accurate nearest neighbor search of Hamming space, so that the search speed is increased.

The invention relates to an unsupervised depth hashing method based on target detection, and belongs to the technical field of computer information retrieval and picture retrieval. The method comprises the steps of obtaining the object tags existing in the pictures by utilizing the target detection, taking the tags as pseudo tags of the pictures, and training a designed end-to-end depth hash modelbased on the pseudo tags to obtain the hash code representation of each picture in a Hamming space; evaluating the quality of the deep Hash model through the average accuracy mean value of the corresponding Hash codes in the picture retrieval task, wherein the average accuracy rate mean value is the MAP, and the unsupervised deep hash model comprises a target detection algorithm unit and a hash network unit. According to the method, the more instructive information can be obtained, the capability of a depth model can be fully utilized to learn the high-quality Hash codes with maintained similarity, and the picture retrieval is carried out in a real picture data set to obtain the best effect, namely, the MAP value is the highest.

Embodiments of this disclosure include an image processing method and apparatus. The method may include obtaining feature points corresponding to training images and feature center points of images in a second quantity of categories and obtaining a feature condition probability distribution that the feature points collide with corresponding feature center points. The method may further include performing network training to obtain target feature center points of the images in the second quantity of categories. The method may further include mapping the first quantity of feature points and the second quantity of target feature center points to a Hamming space, to obtain hash codes of the first quantity of training images and hash center points of the images in the second quantity of categories. The method may further include performing network training using the hash condition probability distribution and the ideal condition probability distribution, to obtain an image hash model.

The invention discloses a universal cross-modal retrieval model based on deep hash. The universal cross-modal retrieval model comprises an image model, a text model, a binary code conversion model and a Hamming space. The image model is used for the feature and semantic extraction of the image data; the text model is used for the feature and semantic extraction of the text data; the binary code conversion model is used for converting the original features into the binary codes; the Hamming space is a common subspace of images and the text data, and the similarity of the cross-modal data can be directly calculated in the Hamming space. According to the universal model for solving cross-modal retrieval by combining deep learning and Hash learning, the data points in an original feature space are mapped into the binary codes in the public Hamming space, similarity ranking is carried out by calculating the Hamming distance between the codes of the data to be queried and the codes of the original data, and therefore a retrieval result is obtained, and the retrieval efficiency is greatly improved. The binary codes are used for replacing the original data storage, so that the requirement of the retrieval tasks for the storage capacity is greatly reduced.