Image retrieval method based on multi-task hash learning

Abstract

The invention discloses an image retrieval method based on multi-task hash learning. Firstly, the deep convolutional neural network model is determined. Secondly, the loss function is designed by using multi-task learning mechanism. Then, the training method of convolutional neural network model is determined, in combination with the loss function, and back propagation method is used to optimize the model. Finally, the image is input to the convolutionalal neural network model, and the output of the model is transformed into hash code for image retrieval. The convolutional neural network modelis composed of a convolutional sub-network and a full connection layer. The convolutional subnetwork consists of a first convolutional layer, a maximum pooling layer, a second convolutional layer, anaverage pooling layer, a third volume base layer and a spatial pyramid pooling layer. The full connection layer is composed of a hidden layer, a hash layer and a classification layer. The training method of the model includes two training methods: a combined training method and a separated training method. The method of the invention can effectively retrieve single tag and multi-tag images, and the retrieval performance is better than other deep hashing methods.

Description

technical field [0001] The invention belongs to the technical field of image retrieval and relates to an image retrieval method based on multi-task hash learning. Background technique [0002] Considering the ever-increasing digital image resources on the Internet, using linear search to retrieve information in such a huge image library will cause huge computing and storage overheads. Therefore, in the application of CBIR technology, the "curse of dimensionality" "Problems happen from time to time. In order to solve this problem, in recent years, the approximate nearest neighbor search has gradually become a part of the focus of researchers, and the hash method is a typical representative of it. The goal of the hash method is usually to let the initial image data be calculated by a hash function to obtain a series of fixed-length binary codes, so as to achieve dimensionality reduction for image representation and help reduce storage overhead. In the phase of similarity cal...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to provide an image retrieval method based on multi-task hash learning, which solves the problem of the deep hash image retrieval method in the prior art, which leads to insufficient accuracy of retrieval images due to insufficient utilization of sample supervision information. High problem, effectively improve the accuracy of image retrieval

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