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
%0 Conference Paper
%1 Puzicha1997
%A Puzicha, J
%A Hofmann, T
%A Buhmann, J M
%B Computer Vision and Pattern Recognition, 1997. Proceedings., 1997
IEEE Computer Society Conf. on
%D 1997
%K Gabor analysisBrodatz-like approach, bank, based filter homogeneity image images, information measures, micro-textures, multi-scale nonparametric real-word retrieval retrieval, segmentation segmentation,image similarity similarity, statistical systems, tests, texture unified unsupervised
%P 267--272
%R 10.1109/CVPR.1997.609331
%T Non-parametric similarity measures for unsupervised texture segmentation
and image retrieval
%X 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
@inproceedings{Puzicha1997,
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},
added-at = {2013-09-29T14:16:50.000+0200},
author = {Puzicha, J and Hofmann, T and Buhmann, J M},
biburl = {https://www.bibsonomy.org/bibtex/2ce8fb7346f341d2c450af2bd0ab5bbe1/guillem.palou},
booktitle = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997
IEEE Computer Society Conf. on},
doi = {10.1109/CVPR.1997.609331},
interhash = {006ca57da62a88f44cd1716f00739c79},
intrahash = {ce8fb7346f341d2c450af2bd0ab5bbe1},
keywords = {Gabor analysisBrodatz-like approach, bank, based filter homogeneity image images, information measures, micro-textures, multi-scale nonparametric real-word retrieval retrieval, segmentation segmentation,image similarity similarity, statistical systems, tests, texture unified unsupervised},
pages = {267--272},
timestamp = {2013-09-29T14:16:50.000+0200},
title = {{Non-parametric similarity measures for unsupervised texture segmentation
and image retrieval}},
year = 1997
}