A Large-Scale Image Retrieval Method

Abstract

The invention discloses a large-scale image retrieval method, comprising the following steps: image feature extraction; hash function projection vector learning; hash function offset learning; image feature dimensionality reduction; image feature encoding; image retrieval. The invention can quickly retrieve large-scale images. First, by learning a discriminative hash function, the discriminability between codes is improved, so as to better distinguish image features of different categories; second, the hash function is used to reduce the dimensionality and code of image features to reduce the storage of image features Computational overhead of the demand and retrieval process. The invention realizes efficient and accurate large-scale image retrieval, and thus has high use value.

Description

technical field

[0001] The invention belongs to the field of computer image retrieval, in particular to a large-scale image retrieval method. Background technique

[0002] With the rapid development of the Internet, various network resources are increasingly abundant, and the scale of network data is also growing exponentially. Among the various types of data on the Internet, images account for the majority, and have reached a massive scale: in 2010, the total number of images counted by the famous website Flickr exceeded 5 billion. Such data continues to grow at an alarming rate, and will reach an unimaginable scale in a few years. Undoubtedly, it is very important to quickly and accurately search the data needed by users from such a large database, but there are also huge difficulties. For example, given an image, how to quickly and accurately search for images similar to the given image in a large-scale database is a hot research topic at present. However, there are oft...

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