RIS ID
21895
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 Details
This conference paper was originally published as Tivive, F, Bouzerdoum, A, A Nonlinear Feature Extractor for Texture Segmentation, IEEE International Conference on Image Processing ICIP 2007, Vol 2, 16-19 Sep, 11-37.