Integrating image clustering and memory indexing for large scale content-based image retrieval

Wenbing Tao; Hai Jin; Feng Luo; Kun Wu

doi:10.1117/12.834309

30 October 2009 Integrating image clustering and memory indexing for large scale content-based image retrieval

Wenbing Tao, Hai Jin, Feng Luo, Kun Wu

Author Affiliations +

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749853 (2009) https://doi.org/10.1117/12.834309
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Wenbing Tao, Hai Jin, Feng Luo, and Kun Wu "Integrating image clustering and memory indexing for large scale content-based image retrieval", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749853 (30 October 2009); https://doi.org/10.1117/12.834309

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