Self-organization neural network for multiple texture image segmentation
Woobeom Lee, Wookhyun Kim · 2003
Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape and depth perception. But no efficient methods captures all aspects of the very diverse texture family including natural scenes. We propose a novel approach for efficient texture image analysis that use unsupervised learning schemes for the texture recognition task. The self-organization neural network for texture image identification is based on features that is extracted at angle and magnitude in the orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, we have attempted to build various texture images. The experimental results show that the performance of the system is very successful.