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Proceedings Paper

Integrating image clustering and memory indexing for large scale content-based image retrieval
Author(s): Wenbing Tao; Hai Jin; Feng Luo; Kun Wu
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Paper Abstract

Content-Based Image Retrieval (CBIR) is an important research topic of information retrieval, involved in computer graphics, image processing, data mining and pattern recognizing. To make content-based image retrieval suitable large-scale image database, we develop an effective dynamic hierarchical clustering index scheme. Although this system uses a hierarchical clustering technology, with the increasing in the number of cluster centers, it is slow to find the centers, and it becomes a system performance bottleneck. In this paper, content features of image memory indexing is built. This method effectively improves the retrieval speed without loss of the precision. Moreover, the clustering model was improved, integrating the content features and textual features of image, which greatly improve the accuracy of the clustering, thus significantly improves the system precision.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749853 (30 October 2009); doi: 10.1117/12.834309
Show Author Affiliations
Wenbing Tao, Huazhong Univ. of Science and Technology (China)
Hai Jin, Huazhong Univ. of Science and Technology (China)
Feng Luo, Huazhong Univ. of Science and Technology (China)
Kun Wu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7498:
MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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