Image retrieval method, terminal and storage device

Abstract

The invention discloses an image retrieval method, a terminal and a storage device. The method comprises the steps of extracting a target feature vector of a to-be-retrieved image; querying a trainedcoarse codebook table, and determining a plurality of target coarse clustering center points closest to the target feature vector and a target sample identification code corresponding to each target coarse clustering center point; querying the trained sub-codebook table by using the plurality of target coarse clustering center points to obtain a plurality of target sub-clustering center points; and performing query and calculation through the target feature vector, the plurality of target coarse clustering center points, the plurality of target sub-clustering center points, the target sample identification code and a pre-calculated distance table to obtain a preset number of retrieval results closest to the target feature vector, the distance table comprising the sample identification codeand a corresponding distance value. According to the method, the data processing amount is reduced to a great extent in a product quantization mode, and the search precision is ensured.

Description

technical field [0001] The present application relates to the technical field of image retrieval, in particular to an image retrieval method, terminal and storage device. Background technique [0002] With the rapid development of computers, Internet, and multimedia in recent years, there are more and more massive image data in the world. In order to effectively analyze and utilize the value of these images, all walks of life are deeply researching efficient and accurate feature retrieval technologies, such as search engines, e-commerce platforms, security, and information authentication fields. [0003] Search images by image, based on an image, search for multiple images that are most similar to it. Compared with text search, image search needs to indirectly use the key information implicit in its own content - feature value. In order not to lose more details, the feature values ​​extracted from the image are generally high-dimensional vector data, that is, high-dimensio...

AI Technical Summary

Problems solved by technology

[0004] This application provides an image retrieval method, terminal and storage device to solve the problem of low efficiency and accuracy of existing image search methods

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