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Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

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Abstract

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.

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Acknowledgements

This work is fully supported by a grant of project No. 7001599–570 from City University of Hong Kong. We would like to thank James Z. Wang for providing us COREL database.

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, People’s Republic of China

    Tommy W. S. Chow, M. K. M. Rahman & Sitao Wu

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  1. Tommy W. S. Chow
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  2. M. K. M. Rahman
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  3. Sitao Wu
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Correspondence to Tommy W. S. Chow.

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Chow, T.W.S., Rahman, M.K.M. & Wu, S. Content-based image retrieval by using tree-structured features and multi-layer self-organizing map. Pattern Anal Applic 9, 1–20 (2006). https://doi.org/10.1007/s10044-005-0019-1

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  • DOI: https://doi.org/10.1007/s10044-005-0019-1

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