| Paper: | MP-L2.2 |
| Session: | Image And Video Segmentation II: Texture Segmentation |
| Time: | Monday, September 17, 14:50 - 15:10 |
| Presentation: |
Lecture
|
| Title: |
A NONLINEAR FEATURE EXTRACTOR FOR TEXTURE SEGMENTATION |
| Authors: |
Fok Hing Chi Tivive; University of Wollongong | | |
| | Abdesselam Bouzerdoum; University of Wollongong | | |
| 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. |
