Texture descriptors for the generic pattern classification problem

Good feature extraction methods are key in many pattern classification problems since the quality of pattern representations affects classification performance. Unfortunately, feature extraction is mostly problem dependent, with different descriptors typically working well with some problems but not with others. In this work, we propose a generalized framework that utilizes matrix representation for extracting features from patterns that can be effectively applied to very different classification problems. The idea is to adopt a two-dimensional representation of patterns by reshaping vectors into matrices so that powerful texture descriptors can be extracted. Since texture analysis is one of the most fundamental tasks used in computer vision, a number of high performing methods have been developed that have proven highly capable of extracting important information about the structural arrangement of pixels in an image (that is, in their relationships to each other and their environment). In this work, first, we propose some novel techniques for representing patterns in matrix form. Second, we extract a wide variety of texture descriptors from these matrices. Finally, the proposed approach is tested for generalizability across several well-known benchmark datasets that reflect a diversity of classification problems. Our experiments show that when different approaches for transforming a vector into a matrix are combined with several texture descriptors the resulting system works well on many different problems without requiring any ad-hoc optimization. Moreover, because texture-based and standard vector-based descriptors preserve different aspects of the information available in patterns, our experiments demonstrate that the combination of the two improves overall classification performance. The MATLAB code for our proposed system will be publicly available to other researchers for future comparisons.