Method for obtaining global feature descriptors

Abstract

The invention provides a method for obtaining global feature descriptors. The method comprises the steps of obtaining the local feature descriptors of an image to be processed and using the local feature descriptors to from a descriptor set, carrying out conversion on all the local feature descriptors in the descriptor set according to a global feature descriptor generating rule to obtain an accumulation gradient vector set including accumulation gradient vectors, and structuring one global feature descriptor of the image to be processed according to the accumulation gradient vectors in the accumulation gradient vector set. The global feature descriptor obtained through the method includes the statistical information of the local feature descriptors of the image to be processed, has strong discriminating ability and improves the processing performance of image retrieval or image classification.

Description

technical field [0001] Embodiments of the present invention relate to the field of computers, and in particular, to a method for acquiring global feature descriptors. Background technique [0002] With the development of smart terminals, there are more and more mobile visual search applications. At present, the image retrieval method based on the intelligent terminal is mainly based on the local feature descriptor of the image, including: 1) extracting the local feature descriptor of the image on the mobile client; 2) compressing the extracted local feature descriptor; 3) The compressed local feature descriptor is transmitted to the server through the network, 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 above-mentioned image retrieval method requires a high amount of computation for local feature compression and establishment of index files such as i...

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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

Furthermore, due to the relatively 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 image retrieval efficiency. efficiency

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