Supervised texture segmentation using DT-CWT and a modified k-NN classifier

Brian W. Ng; Abdesselam Bouzerdoum

doi:10.1117/12.386707

30 May 2000 Supervised texture segmentation using DT-CWT and a modified k-NN classifier

Brian W. Ng, Abdesselam Bouzerdoum

Proceedings Volume 4067, Visual Communications and Image Processing 2000; (2000) https://doi.org/10.1117/12.386707
Event: Visual Communications and Image Processing 2000, 2000, Perth, Australia

Abstract

Texture segmentation has been an important problem in image processing. Filtering approaches have been popular, and recent studies have indicated a need for efficient, low- complexity algorithms. In this paper, we present a texture segmentation scheme based on the Dual-Tree Complex Wavelet Transform (DT-CWT). The advantage of the DT-CWT over other approaches is that it offers a partially redundant representation with strong directionality. The texture segmentation scheme presented here consists of three steps: feature extraction, conditioning, and classification. A number of feature smoothing windows have been tested. Classification is performed using a modified K-NN clustering algorithm. The proposed scheme consistently achieves error rates of less than 10%.

Citation Download Citation

Brian W. Ng and Abdesselam Bouzerdoum "Supervised texture segmentation using DT-CWT and a modified k-NN classifier", Proc. SPIE 4067, Visual Communications and Image Processing 2000, (30 May 2000); https://doi.org/10.1117/12.386707

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