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.