Multi-label image retrieval method fusing triple loss and generative adversarial network

Abstract

The invention provides a multi-label image retrieval method fusing triple loss and generative adversarial network, which comprises the following steps of: establishing a deep learning framework, and deploying a generative adversarial network model; inputting the image data set into the generative adversarial network model to obtain a multi-label image and triple data; constructing a triple loss function based on the multi-label image; selecting a first image from the image data set to train the deep hash coding network so as to obtain a trained deep hash coding network; selecting a preset number of second images from the image data set, and inputting the second images into the trained deep hash coding network to obtain a hash vector database; and inputting a first image to be retrieved into the trained deep hash coding network to retrieve a second image similar to the first image. According to the invention, the generative adversarial network is used to generate the multi-label generation picture similar to the data set sample, the training data volume is expanded, and the retrieval speed and precision of the image are improved.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to a multi-label image retrieval method that integrates triplet loss and generative confrontation network. Background technique [0002] With the explosive growth of image and video data on the Internet, large-scale image retrieval tasks have received increasing attention in recent years. The main task of the image retrieval system is not only to ensure the image quality in the retrieval results, but also to ensure the efficiency of retrieval, and at the same time, it is also necessary to solve how to efficiently store massive information, so that users can have a better experience. [0003] Representing images efficiently is an important task for large-scale image retrieval. Binary hashing has gained a lot of attention due to the computational efficiency and storage efficiency of binary hash codes. Its goal is to map high-dimensional image data into the same Hamming space while mai...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the shortcomings of the existing methods, the present invention proposes a multi-label image retrieval method that integrates triplet loss and generative adversarial networks to solve the problems existing in the prior art. the above question

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