Studies On Texture Feature Measurement And Classification

In this paper, the classifications of textured materials are studied. Texture classification is of great challenge in general. In the first chapter, the background of this paper is described. In the second chapter, the general methods of feature measurement of texture are provided.In chapter three, firstly, the filter bank is exploited to generate features, and the filter responses are described as the feature of texture. Secondly, the two classification approaches: the Maximum Weight Dependence Trees and the Naive Bayes classification are explored. Finally, the experiments demonstrated that the method of Maximum Weight Dependence Trees (MWDT) had a better result. And MWDT can be a powerful tool for texture classification.In chapter four, the spare texture representation is described by the local affine regions. This is a significant difference from the traditional feature extraction framework, which only involves processing every pixel location in the 2D image. In this paper, a new texture representation is developed that is invariant to geometric transformation and it can be locally approximated by an affine model. Then, the two classification approaches: the Naive Bayes classification and the improved classification are explored. The proposed approach is evaluated in retrieval and classification tasks by the entire CUReT database.In chapter five, based on homography in computer vision, a geometric correction method of sparse texture is discussed, which used feature points of the texture. At the feature extraction stage, Using RANSAC (RANdom Sample Consensus) estimates the homography mapping, which doesn't need lots of sample for feature measurement. Our experimental demonstrates that this chapter's method is a powerful tool for texture classification.