Wold Features for Unsupervised Texture Segmentation

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

Citing 10
Cited 3

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Abstract

An efficient texture representation for unsupervised segmentation is addressed based on the concept of Wold decomposition. Textures are described by the wavelet tuned to various scales and rotations to describe its deterministic component, and by the autogressive model to describe its indeterministic component. The wavelet features and the AR parameters capturing the perceptual properties, "periodicity", "directionality", and "randomness", respectively, have been proved to consistent with human texture perception. The performance of our approach is demonstrated on Brodatz textures and natural textured images.