cross-modal Hash learning method based on an anchor point graph

Abstract

A cross-modal hash learning method based on anchor map, characterized in that the method comprises the following steps: (1) using an objective function designed based on anchor map technology Binaryhash encoding of objects in image modal and text modal and the projection matrix of the image modal and text modal; (2) In view of the non-convex nature of the objective function, solving the unknownvariable in the objective function by alternate updating; (3) projection matrix based on the image modal and text modal obtained by the solution and generating a binary hash code for the sample of the query and the samples in the retrieved sample set; (4) Calculating the Hamming distance of the query sample to each sample in the sample set based on the generated binary hash code; (5) performing the retrieval of the query sample using the cross-modal retriever based on the approximate nearest neighbor search. The method can quickly obtain an approximate matrix of the true similarity matrix based on the anchor map technique.

Description

technical field [0001] The invention relates to a cross-modal hash learning method based on an anchor graph. Background technique [0002] With the rapid development of information technology, human society has entered the era of big data, and massive data from different fields and different applications will be generated all the time. Facing the explosive growth of data, how to quickly retrieve the required information to ensure the effective use of data has become an urgent and very challenging problem to be solved in the era of big data. [0003] Nearest neighbor search, also known as similarity search, plays an important role in many applications such as document retrieval, object recognition, and approximate image detection. Among the methods for approximate nearest neighbor search, the hash-based search (retrieval) method has received more and more attention in recent years. The hash-based search method can map high-dimensional feature data into compact binary hash c...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a cross-modal hash learning method based on anchor graphs, which solves the problem that existing cross-modal hash learning methods have not completely solved the problem of maintaining features based on graph structures on large-scale data sets. The problem of beneficial information in the data, and the problem of discriminative feature selection involved in mapping raw feature data from high-dimensional feature space to low-dimensional Hamming space, propose a cross-modal hashing learning method based on anchor graph and applied to cross-modal retrieval tasks involving image modality and text modality

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