The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Non-parametric similarity measures for unsupervised texture segmentation and image retrieval
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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Cites
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Cited by
Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498
Dollar, P., Tu, Z., Perona, P., & Belongie, S. (2009) · Procedings of the British Machine Vision Conference 2009 · doi:10.5244/c.23.91 · Cited by 1,237
Metadata
- Authors
- Puzicha, J., Hofmann, T., & Buhmann, J. M.
- Venue
- Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-22
- Pages
- 267-272
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1528775006
- Cited by
- 254
- References
- 1
- FWCI
- 7.44
- Type
- conference paper
- Language
- en
- Open access
- closed
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