@guillem.palou

Non-parametric similarity measures for unsupervised texture segmentation and image retrieval

, , and . Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conf. on, page 267--272. (1997)
DOI: 10.1109/CVPR.1997.609331

Abstract

In this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi-scale Gabor filter bank. We demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer a unified approach to these closely related tasks. We present results on Brodatz-like micro-textures and a collection of real-word images

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