基于局部谱能量自相似矩阵的纹理描述  

Texture description based on local spectrum energy self-similarity matrix

作　　者：杨鸿波[1] 侯霞[2] 

机构地区：[1]北京信息科技大学电子信息与控制实验教学中心,北京100192 [2]北京信息科技大学计算机学院,北京100101

出　　处：《计算机应用》2014年第3期790-796,共7页journal of Computer Applications

基　　金：北京市属高等学校高层次人才引进与培养计划项目的青年拔尖人才培育计划项目(CIT&TCD201304115)

摘　　要：对于纹理检测和分类中的纹理描述问题,提出一种新的基于Gabor滤波器组局部谱能量的自相似矩阵来描述纹理的方法。首先采用多尺度、方向的极坐标对数Gabor滤波器组对纹理模板进行滤波,获得频域上局部频段和方向上的纹理信息;然后计算频域上各尺度、方向上局部谱能量的自相似度量,将这些度量值以自相似矩阵的形式进行存储,并作为纹理特征的描述子;最后将这种描述方法应用到纹理检测和分类中。由于该描述子主要体现的是纹理模板在不同频段和方向局部谱能量的自相似程度,所以它对滤波器参数的依赖度较低。实验中利用纹理特征描述子可以实现比较准确的纹理检测,多类纹理合成图像分类实验的准确率达到了91%以上。实验结果说明,纹理局部谱能量的自相似矩阵是一种十分有效的纹理描述方法,其检测和分类的结果对后期的纹理分割、纹理识别等研究领域具有广泛的应用前景。To deal with the texture detection and classification problem, a new texture description method based on self- similarity matrix of local spectrun~ 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.

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 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]