  In this paper, we introduce a non-parametric texture similarity measure based
on the singular value decomposition of the curvelet coefficients followed by a
content-based truncation of the singular values. This measure focuses on images
with repeating structures and directional content such as those found in
natural texture images. Such textural content is critical for image perception
and its similarity plays a vital role in various computer vision applications.
In this paper, we evaluate the effectiveness of the proposed measure using a
retrieval experiment. The proposed measure outperforms the state-of-the-art
texture similarity metrics on CURet and PerTEx texture databases, respectively.
