结合Gabor滤波器和扩展LTP算子的无监督纹理图像分割  

Unsupervised segmenting texture images based on Gabor filters and extended LTP operator

作　　者：马瑞[1,2] 周力 Ma Rui;Zhou Li(Department of Information and Intelligent Engineering,Anhui Vocational College of Electronics and Information Technology,Bengbu 233030,China;School of Computer Science and Information Technology,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]安徽电子信息职业技术学院信息与智能工程系,蚌埠233030 [2]合肥工业大学计算机与信息学院,合肥230601

出　　处：《中国图象图形学报》2020年第3期442-455,共14页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61702154);安徽省自然科学基金项目(1808085QF189);安徽省高等学校自然科学研究项目(KJ2018A0779)。

摘　　要：目的现实中的纹理往往具有类型多样、形态多变、结构复杂等特点,直接影响到纹理图像分割的准确性。传统的无监督纹理图像分割算法具有一定的局限性,不能很好地提取稳定的纹理特征。本文提出了基于Gabor滤波器和改进的LTP(local ternary pattern)算子的针对复杂纹理图像的纹理特征提取算法。方法利用Gabor滤波器和扩展LTP算子分别提取相同或相似纹理模式的纹理特征和纹理的差异性特征,并将这些特征融入到水平集框架中对纹理图像进行分割。结果通过实验表明,对纹理方向及尺度变化较大的图像、复杂背景下的纹理图像以及弱纹理模式的图像,本文方法整体分割结果明显优于传统的Gabor滤波器、结构张量、拓展结构张量、局部相似度因子等纹理分割方法得到的结果。同时,将本文方法与基于LTP的方法进行对比,分割结果依然更优。在量化指标方面,将本文方法与各种无监督的纹理分割方法就分割准确度进行对比,结果表明,在典型的纹理图像上,本文方法准确度达到97%以上,高于其他方法的分割准确度。结论提出了一种结合Gabor滤波器和扩展LTP算子的无监督多特征的纹理图像分割方法,能够较好地提取相似纹理模式的特征和纹理的差异性特征,且这些纹理特征可以很好地融合到水平集框架中,对真实世界复杂纹理图像能够得到良好的分割效果。Objective The texture is often characterized by various unregular types,varied shapes,and complex structures,which directly weaken the accuracy of texture image segmentation. The semantic segmentation methods based on deep learning need benchmark training sets. Constructing the training sets composed by the complex and diverse texture images is difficult. Therefore,utilizing the unsupervised image segmentation methods to solve the problem of texture segmentation is necessary. However,the traditional unsupervised texture image segmentation algorithms have limitations and cannot be used to effect tivelyextract the stable texture features. Method Based on the idea that the Gabor operator can extract the texture diversity features and the local ternary pattern(LTP) operators imply the threshold differences,combining Gabor filters with extended LTP operators is proposed to describe texture diversity features in this paper. Gabor filter is used to extract the texture features of the same or similar texture patterns. Then,the texture difference features are extracted. Compared with the traditional LTP,the main advantages of the extended LTP operator are embodied in two aspects. On the one hand,the extended size make the LTP operator effective in image segmentation based on the size features of the segmented image. On the other hand,the weights are given to each position of the extended LTP operator. The exponential weight differences are given according to the distances between each position and the central point. Finally,these extracted features are integrated into the level set frame to segment the texture image. The advantages of the proposed method are described as follows: First,the extended LTP operator can effectively extract the texture difference features of local regions. Second,the Gabor filter and extended LTP operator are complementary. The main contributions of the proposed method in this paper are elaborated in the following: 1) By improving the traditional LTP operator,we propose an extended LTP operator

关 键 词：纹理图像分割 GABOR滤波器 扩展LTP(local ternary pattern)算子 无监督 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]