Description: Feature extraction is a key issue in contentbased
image retrieval (CBIR). In the past, a number of
texture features have been proposed in literature,
including statistic methods and spectral methods.
However, most of them are not able to accurately capture
the edge information which is the most important texture
feature in an image. Recent researches on multi-scale
analysis, especially the curvelet research, provide good
opportunity to extract more accurate texture feature for
image retrieval. Curvelet was originally proposed for
image denoising and has shown promising performance.
In this paper, a new image feature based on curvelet
transform has been proposed. We apply discrete curvelet
transform on texture images and compute the low order
statistics from the transformed images. Images are then
represented using the extracted texture features. Retrieval
results show, it significantly outperforms the widely used
Gabor texture feature.
- [cbir] - image retrieval article is doctoral thes
- [5] - Image denoising, adaptive thresholding m
- [fdct_usfft_cpp] - This directory includes matlab interface
- [fdct_wrapping_denoise] - Curvelet transform for image denoising,
- [gann] - zh is a genetic algorithm with neural ne
- [edgedir_his] - Edge direction histogram based image ret
- [contourlet_toobox] - Image feature extraction toolbox of Cont
- [liuwei] - Sift-based Image Retrieval, other sites
- [CWT] - Wavelet Transform curvelet
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mmsp08.pdf
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