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Rotation Invariant Curvelet Features for Region Based Image Retrieval

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Abstract

There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.

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Authors and Affiliations

  1. Gippsland School of Information Technology, Monash University, Churchill, Victoria, 3842, Australia

    Dengsheng Zhang, M. Monirul Islam, Guojun Lu & Ishrat Jahan Sumana

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  1. Dengsheng Zhang
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  2. M. Monirul Islam
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  3. Guojun Lu
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  4. Ishrat Jahan Sumana
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Correspondence to Dengsheng Zhang.

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Zhang, D., Islam, M.M., Lu, G. et al. Rotation Invariant Curvelet Features for Region Based Image Retrieval. Int J Comput Vis 98, 187–201 (2012). https://doi.org/10.1007/s11263-011-0503-6

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