Matrix representation in pattern classification
Presented in this paper is a novel feature extractor technique based on texture descriptors. Starting from the standard feature vector representation, we study different methods for representing a pattern as a matrix. Texture descriptors are then used to represent each pattern. We examine a variety of local ternary patterns and local phase quantization texture descriptors. Since these texture descriptors extract information using subwindows of the textures (i.e. a set of neighbor pixels), they handle the correlation among the original features (note that the pixels of the texture that describes a pattern are extracted starting from the original feature). We believe that our new technique exploits a new source of information. Our best approach using several well-known benchmark datasets, is obtained coupling the continuous wavelet approach for transforming a vector into a matrix and a variant of the local phase quantization based on a ternary coding for extracting the features from the matrix. Support vector machines are used both for the vector-based descriptors and the texture descriptors. Our experiments show that the texture descriptors along with the vector-based descriptors can be combined to improve overall classifier performance.
Management and Information Technology
pattern classification, texture descriptor, locally ternary patterns, local phase quantization, support vector machines
Nanni, Loris, Sheryl Brahnam, and Alessandra Lumini. "Matrix representation in pattern classification." Expert Systems with Applications 39, no. 3 (2012): 3031-3036.
Expert Systems with Applications