An image retrieval method based on deep Hash learning optimization

Abstract

An image retrieval method based on deep Hash learning optimization comprises the following steps of 1, constructing a multi-layer full connection network firstly, connecting a panh function behind each layer of full connection, and finally conducting the sign operation on the network output; 2, constructing a semantic retention loss function obtained by a classification loss function and a weighted semantic similarity matrix, and a target function with discrete orthogonal constraint obtained by a quantitative loss function and a regular item; 3, optimizing an objective function; 4, dividing the obtained feature data set into a query set Q and a to-be-searched set D, taking a part of data in the to-be-searched set D to form a training data set P, inputting depth characteristics and label information of the training data set P, initializing a weight coefficient and a binary code, performing iterative optimization on the step 3 in sequence to obtain an optimal network weight coefficient,and obtaining a depth hash function by the step 2; and step 4, carrying out the image retrieval and precision testing. The method is relatively higher in precision and relatively higher in retrieval efficiency.

Description

technical field [0001] The invention relates to image big data processing and analysis in the field of computer vision, in particular to a deep hash learning optimization algorithm and an image retrieval method. Background technique [0002] With the development of network sharing technology, the promotion of big data technology and the generation of massive images, traditional image retrieval technology has been unable to adapt to large-scale image search, and hash learning has been widely used due to its high efficiency and easy storage. in large-scale image retrieval. Hash learning obtains the hash function from the data through the method of machine learning, thereby mapping the data into binary codes, and retaining the neighbor relationship in the original space as much as possible, that is, maintaining similarity. Because binary discrete optimization is an NP problem, many methods use mixed integer optimization methods to relax binary codes to continuous values ​​for ...

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Problems solved by technology

[0003] In order to overcome the optimization problems existing in existing hash learning methods, the present invention provides an image retrieval method based on deep hash learning optimization with high precision and high retrieval efficiency, using a multi-layer neural network to construct a nonlinear hash function, The objective function under the discrete orthogonal constraint that maintains semantic similarity is constructed, and then the objective function is optimized by ADMM (Alternating Direction Method of Multipliers) optimization method and stochastic gradient descent method, thus obtaining Ha Hive function, using a hash function to convert the target image features into a hash code, and using the hash code for image retrieval when retrieving

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