Method for obtaining compact global feature descriptors of image and image retrieval method

Abstract

The invention provides a method for obtaining compact global feature descriptors of an image and an image retrieval method. The method for obtaining the compact global feature descriptors of the image comprises the following steps: obtaining at least a local feature descriptor of the image; selecting one or more local feature descriptors from all the local feature descriptors; carrying on dimensionality reduction to the selected local feature descriptors; obtaining the local feature descriptors after dimensionality reduction; converting the local feature descriptors after dimensionality reduction into global feature descriptors for expressing visual feature of the image according to a first rule; carrying on data compression to the global feature descriptors; and obtaining the compact global feature descriptors of the image, wherein byte size of the global feature descriptors can be varied according to the variation of parameter values in the first rule. Through the adoption of the method, the obtained global feature descriptors are more compact and have scalability, and the defect that a mobile terminal with lower internal memory has insufficient space in the prior art can be solved.

Description

technical field [0001] Embodiments of the present invention relate to the field of computers, and in particular, to a method for obtaining a compact global feature descriptor of an image and an image retrieval method. Background technique [0002] With the development of smart terminals, there are more and more mobile visual search applications. Currently, image retrieval methods based on smart terminals include: 1) extracting local feature descriptors of images on the mobile client; 2) compressing the extracted local feature descriptors; 3) compressing the compressed local feature descriptors through the network It is transmitted to the server, so that the server searches the database of the server according to the local feature descriptor, and sends the search result to the mobile client. [0003] However, the local feature compression of the above image retrieval method and the establishment of index files such as inverted ranking require a relatively high amount of comp...

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Problems solved by technology

[0003] However, the above-mentioned image retrieval method requires a high amount of calculation for local feature compression and establishment of index files such as inversion. In particular, the local feature descriptor obtained in the image retrieval method takes up a large space. Therefore, according to the current network bandwidth , there is a problem that the mobile client cannot send the local feature descriptor to the server quickly

In addition, due to the large space occupied by the local feature descriptor, the process of searching and matching by the server according to the local feature descriptor transmitted by the mobile client is also very slow, which seriously affects the query response time of the retrieval system and reduces the query efficiency.

[0004] In addition, the existing technology also proposes a method for image retrieval using global visual features. In the process of extracting global feature descriptors, the feature dimensionality reduction method and descriptor compression method used in this method require a large amount of storage space for Storage of dimensionality reduction matrices and quantization tables, etc., cannot be realized on mobile terminals with low memory. At the same time, the length of the global feature descriptor is fixed, which cannot be applied to various retrieval conditions, thereby affecting the performance of image search

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