Hash image retrieval method based on deep learning and low-rank matrix optimization

Abstract

The invention discloses a Hash image retrieval method based on deep learning and low-rank matrix optimization, and the method comprises the following steps: S1, obtaining image data, carrying out themarking and preprocessing of the data, constructing a data set of image retrieval, and dividing the data set into a training set and a test set; S2, constructing a deep feature extraction network, andconstructing a deep Hash network trunk; S3, inputting the training set into a deep Hash network trunk, and constructing a Hash network based on a maximum probability likelihood and a low-rank regularization loss function; S4, training the Hash network; S5, respectively inputting the test set image and the training set image into a Hash network, generating a binary Hash code, and calculating a mutual Hamming distance; and S6, returning a picture with the minimum Hamming distance in the training set as a retrieval result. According to the image retrieval method based on Hash representation, theproblems of ring breakage of similarity information and large quantization error caused by the fact that binary continuous value features are directly coded into a Hamming space are solved, and the performance of the image retrieval method based on Hash representation is improved.

Description

Technical field [0001] The invention belongs to the technical field of hash image retrieval and artificial intelligence, and particularly relates to a hash image retrieval method based on deep learning and low-rank matrix optimization. Background technique [0002] In recent years, Internet technology, intelligent hardware and multimedia technology have achieved unprecedented development, and a large amount of network data has emerged, especially the widespread use of mobile devices, making all kinds of picture data full of network platforms and electronic communication equipment , How to match and retrieve massive image resources more accurately and efficiently, whether in theoretical research or in commercial applications, is of great value, such as recommendation of similar products in e-commerce shopping platforms, face retrieval, etc. [0003] The current image retrieval methods mainly include retrieval based on text tags and retrieval based on image content. Traditional imag...

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Problems solved by technology

[0005] However, the previous methods of this kind all learn continuous feature codes first, and then binarize the continuous feature codes into hash codes through an independent post-processing step. This method cannot learn feature representations that retain similarity well. , and cannot effectively control the quantization error, which greatly limits the improvement of retrieval performance

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