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A nonlinear feature extractor for texture segmentation

Conference Paper


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Abstract


  • This article presents a feed-forward network architecture that can be used as a nonlinear feature extractor for texture segmentation. It comprises two layers of feature extraction units; each layer is arranged into several planes, called feature maps. The features extracted from the second layer are used as the final texture features. The feature maps are characterised by a set of masks (or weights), which are shared among all the units of a single feature map. Combining the nonlinear feature extractor with a classifier, we have developed a texture segmentation system that does not rely on pre-defined filters for feature extraction; the weights of the feature maps are found during a supervised learning stage. Tested on the Brodatz texture images, the proposed texture segmentation system achieves better classification accuracy than some of the most popular texture segmentation approaches.

Publication Date


  • 2007

Citation


  • F. Tivive & A. Bouzerdoum, "A nonlinear feature extractor for texture segmentation," in International Conference on Image Processing, 2007, pp. II-37-II-40.

Scopus Eid


  • 2-s2.0-48149102556

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1649&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/642

Start Page


  • II-37

End Page


  • II-40

Abstract


  • This article presents a feed-forward network architecture that can be used as a nonlinear feature extractor for texture segmentation. It comprises two layers of feature extraction units; each layer is arranged into several planes, called feature maps. The features extracted from the second layer are used as the final texture features. The feature maps are characterised by a set of masks (or weights), which are shared among all the units of a single feature map. Combining the nonlinear feature extractor with a classifier, we have developed a texture segmentation system that does not rely on pre-defined filters for feature extraction; the weights of the feature maps are found during a supervised learning stage. Tested on the Brodatz texture images, the proposed texture segmentation system achieves better classification accuracy than some of the most popular texture segmentation approaches.

Publication Date


  • 2007

Citation


  • F. Tivive & A. Bouzerdoum, "A nonlinear feature extractor for texture segmentation," in International Conference on Image Processing, 2007, pp. II-37-II-40.

Scopus Eid


  • 2-s2.0-48149102556

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1649&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/642

Start Page


  • II-37

End Page


  • II-40