Content-adaptive non-parametric texture similarity measure
Abstract
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.
Cite
@article{arxiv.1811.01980,
title = {Content-adaptive non-parametric texture similarity measure},
author = {Motaz Alfarraj and Yazeed Alaudah and Ghassan AlRegib},
journal= {arXiv preprint arXiv:1811.01980},
year = {2019}
}
Comments
7 pages, 7 Figures, 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)