Method for clustering data in image retrieval system

Abstract

The invention discloses a method for clustering data in an image retrieval system, belonging to the technical field of information processing. The method comprises an off-line process and an on-line process, wherein the off-line process is characterized by extracting an SIFT (Scale Invariant Feature Transform) characteristic for a standard image, then the SIFT characteristic is subjected to off-line clustering processing, and a standard image vector is built by virtue of vectorization processing on the basis of the off-line clustering result; in the on-line process, the SIFT characteristic ofthe image to be retrieved is extracted; then, on the basis of the off-line clustering result, an image vector to be retrieved is obtained by virtue of the vectorization processing; and the image vector to be retrieved is subjected to similarity search in a standard image vector. By utilizing the method, the characteristics of strong representativeness and distinguishable capability in large-scaledata can be quickly obtained, the clustering of the large-scale data is achieved, and newly-added image data is subjected to quick incremental quantity and clustering on the basis of effectively reusing the clustering result of the original image data, thereby finally realizing a high-efficient image retrieval task.

Description

technical field [0001] The invention relates to a method in the technical field of information processing, in particular to a data clustering method in an image retrieval system. Background technique [0002] Early image retrieval systems generally used information such as color, texture, and shape to describe image features. With the deepening of research, scholars began to introduce certain invariant features, such as using classic SIFT (Scale Invariant Feature Transform) features to represent images. information, and can obtain better retrieval results than traditional methods. [0003] The main challenge here is that the amount of image feature data is very large, and it is very inefficient to directly use the original features for retrieval. Traditional text retrieval technology has achieved great success in practical applications, so scholars began to introduce text retrieval technology into image retrieval. At present, the typical method is to cluster the features f...

AI Technical Summary

Problems solved by technology

[0011] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a data clustering method in an image retrieval system, which can quickly obtain features with strong representativeness and distinguishability in large-scale d

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