Nonlinear Hash Image Retrieval Method Based on Binary Discrete Optimization of Equivalent Continuous Variation

Abstract

A nonlinear hash image retrieval method based on binary discrete optimization of equivalent continuous variation, comprising the following steps: step 1, converting the binary discrete optimization problem obtained after the general hash model is quantized into an equivalent continuous optimization problem, Obtain the corresponding optimal solution; step 2, randomly select some images from the image set D to be searched to form a training image set T; step 3, construct a nonlinear hash function through a three-layer fully connected network; step 4, preserve the loss by quantifying similarity function and adding discrete orthogonal constraints and bit balance constraints to obtain a nonlinear hash objective function; step five, optimize the objective function, optimize the network parameters with the stochastic gradient descent method, optimize the binary code using the step one method, and Convergence analysis is given; Step 6, train the hash function; Step 7, calculate the hash code for image retrieval. The invention has small accumulative error, equivalence of continuous optimization problem and binary discrete optimization problem, optimization convergence and high retrieval precision.

Description

technical field [0001] The invention relates to big data processing and analysis in the field of big data and image retrieval in the field of computer vision, and is especially used in discrete optimization methods in hash learning and image retrieval in hash learning. Background technique [0002] With the rapid development of information technology and the promotion of big data technology, the Internet, mobile phones, and logistics networks generate massive image data every day, and the subsequent storage and transmission requirements make traditional image retrieval technology unable to adapt to large-scale In image search, efficient retrieval methods brought about by fast and compact feature representations have been extensively studied. [0003] As a representative method of fast and compact feature representation, hash learning has been widely used in large-scale image retrieval due to its high efficiency and small storage space. Hash learning mainly uses machine lear...

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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing hash learning image retrieval optimization method that the cumulative error is large, the continuous optimization problem and the binary discrete optimization problem are not equivalent, and the optimization does not converge, the present invention provides a continuous optimization method with a small cumulative error Nonlinear Hash Image Retrieval Based on Binary Discrete Optimization with Equivalent Continuous Variation Equivalence and Optimization Convergence of Problem and Binary Discrete Optimization Problem

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