Matrix representation in pattern classification

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pattern classification, texture descriptor, locally ternary patterns, local phase quantization, support vector machines


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.

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Nanni, Loris, Sheryl Brahnam, and Alessandra Lumini. "Matrix representation in pattern classification." Expert Systems with Applications 39, no. 3 (2012): 3031-3036.

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Management and Information Technology