TY - GEN
T1 - A nonlinear feature extractor for texture segmentation
AU - Tivive, Fok Hing Chi
AU - Bouzerdoum, Abdesselam
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Image texture analysis
KW - Neural network architecture
KW - Nonlinear filters
KW - Pattern recognition
UR - https://www.scopus.com/pages/publications/48149102556
U2 - 10.1109/ICIP.2007.4379086
DO - 10.1109/ICIP.2007.4379086
M3 - Conference contribution
AN - SCOPUS:48149102556
SN - 1424414377
SN - 9781424414376
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - II37-II40
BT - 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
T2 - 14th IEEE International Conference on Image Processing, ICIP 2007
Y2 - 16 September 2007 through 19 September 2007
ER -