Image optimization clustering method based on typical correlation analysis

Abstract

The invention belongs to the cross-media information technology field and particularly is an image optimization clustering method based on the typical correlation analysis. The invention mainly adopts the typical correlation to analyze while considering content characteristics of media data in various modes, maps the characteristics of the media data in various modes to an isomorphism sub-space of a united dimension through the sub-space mapping algorithm and obtains the final clustering result through optimizing clustering algorithm. The invention overcomes single-mode characteristic limitation in the multimedia field where only data is used, effectively solves the isomerism problem of the media data in various modes on the bottom layer characteristics, realizes the united measurement of the media object information between various modes, obtains results which are more accurate, more effective and more comforted to the needs in the large scale image data and has a wide application value in the cross-media processing and the retrieval field.

Description

technical field [0001] The invention belongs to the technical field of cross-media information, and in particular relates to an image optimization clustering method based on typical correlation analysis. Background technique [0002] With the development of the Internet and informatization, the capacity and quantity of digital images are increasing rapidly, and massive image data are generated every day. Although the increase of images provides more resources, people find it more and more difficult to obtain the image information they need, which means that image organization, management, and processing on the Internet have brought more and more difficult problems. problem. In such a background, it is particularly important to organize and manage a large number of images through effective algorithms, and provide people with effective ways to obtain image services [1] . [0003] In the current image organization and management system on the Internet, two different types of...

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Problems solved by technology

[0016] The traditional k-means algorithm is a classic clustering algorithm that assumes the Euclidean space and the number k of the final clusters is known in advance. The k-means algorithm is a faster algorithm in the clustering algorithm, but in When the data size is extremely large, its execution efficiency will still be low due to a large number of repeated calculations

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