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Image optimization clustering method based on typical correlation analysis

A technology of canonical correlation and clustering method, applied in the field of image optimization clustering based on canonical correlation analysis, can solve problems such as low execution efficiency, and achieve the effect of fast clustering, strong applicability and high correlation

Inactive Publication Date: 2014-11-26
FUDAN UNIV
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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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  • Image optimization clustering method based on typical correlation analysis
  • Image optimization clustering method based on typical correlation analysis
  • Image optimization clustering method based on typical correlation analysis

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Embodiment Construction

[0054] The image optimization clustering method based on typical correlation analysis of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] (1) Collection data object

[0056] Collect data objects, that is, obtain images and image annotation data, and sort out image annotation data that do not appear frequently or are useless in the entire data set. Generally, the obtained data set contains a lot of noise data, so it should be properly processed and filtered before using these data for feature extraction. For images, the obtained images are all in a uniform JPG format, and no conversion is required. For text annotation of images, the resulting image annotations contain a lot of meaningless words, such as words plus numbers without any meaning. Some images have as many as dozens of annotations. In order for the image annotations to describe the main information of the image well, those useless and meaningless annotati...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 张玥杰毛文辉朱勤恩李杨金城薛向阳张涛
Owner FUDAN UNIV
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