Rotation invariant curvelet features for texture image retrieval

Effective texture feature is an essential component in any content based image retrieval system. In the past, spectral features, like Gabor and wavelet, have shown superior retrieval performance than many other statistical and structural based features. Recent researches on multi-resolution analysis have found that curvelet captures texture properties, like curves, lines, and edges, more accurately than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance significantly, especially in cases where there are many similar images with different orientations. This paper analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. Experimental results show that the new rotation invariant curvelet feature outperforms the curvelet feature without rotation invariance.