Nonparametric Multichannel Texture Description with Simple Spatial Operators

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ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

Citing 2
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Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions

Pattern Recognition Letters
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)

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Abstract

A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggregate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with Brodatz textures and MeasTex test suites the proposed method performs favorably compared to GLCM, Gabor and GMRF features.