Large-similarity image similarity retrieval method and system based on deep product quantization

Abstract

The invention puts forward a large-similarity image similarity retrieval method based on deep product quantization. The method comprises the following steps that: inputting a picture to be retrieved into a trained deep neural network, and obtaining feature representation corresponding to the picture to be retrieved, wherein the deep neural network comprises that the last fully connected layer before a multiple-term relative rate regression classifier in AlexNet is replaced with a fully connected quantization layer with a plurality of units; on the basis of the feature representation corresponding to the picture to be retrieved and the feature representation corresponding to each picture in a retrieval library, calculating an asymmetric quantization distance between a picture to be retrieved and each picture in the retrieval library; and in the retrieval library, selecting a preset quantity of pictures which have the shortest asymmetric quantization distance with the picture to be retrieved from the retrieval library as a retrieval result. By use of the method, a quantization error is minimum on the basis of deep representation learning, the quantifiable property of deep features can be obviously improved, and therefore, retrieval accuracy and time efficiency are greatly improved.

Description

technical field [0001] The present invention relates to the technical field of computer data management, more specifically, to a large-scale image similarity retrieval method and system based on depth product quantization. Background technique [0002] In the Internet era, with the continuous increase of multimedia resources on the Internet, how to quickly and effectively find relevant data from large-scale data is a great test both in terms of time and space. With the rapid development of the Internet, large-capacity, high-dimensional image big data is becoming more and more common in search engines and social networks, and has attracted more and more attention. How to quickly and effectively perform image retrieval and analysis is an urgent need to solve The problem of approximate nearest neighbor query is just for this problem, and how to ensure the calculation efficiency and search quality at the same time is the key of approximate nearest neighbor query. A very common ...

AI Technical Summary

Problems solved by technology

[0004] However, the deep hashing method provided by the existing technology cannot statistically minimize the quantization error, so that it cannot improve the quantifiability of image depth features in retrieval, resulting in low retrieval accuracy

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