关键词: 自相似矩阵, 纹理描述, 局部谱能量, 纹理检测, 纹理分类

Abstract:

To deal with the texture detection and classification problem, a new texture description method based on self-similarity matrix of local spectrum energy of Gabor filters bank output was presented. Firstly, local frequency band and orientation information of texture template were obtained by convolving template with polar LogGabor filters bank. And then the self-similarities of different local frequency patches were measured and stored in a self-similarity matrix which was defined as the texture descriptor in this paper. At last this texture descriptor could be used in texture detection and classification. Due to the reflection of self-similarity level of different bands and orientations, the descriptor had lower dependency of Gabor filters bank parameters. In the tests, this descriptor produced better detection results than Homogeneous Texture Descriptor (HTD) and the other self-similarity descriptors and the accuracy of multi-texture classification could be up to 91%. The experimental results demonstrate that self-similarity matrix of local power spectrum is a kind of effective texture descriptor. The output of texture detection and classification can be used widely in the post texture analysis task, such as texture segmentation and recognition.

Key words: Self-Similarity Matrix (SSM), texture description, local spectrum energy, texture detection, texture classification