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.

L. Nanni, S. Brahnam, A. Lumini (2012). Matrix representation in pattern classification. EXPERT SYSTEMS WITH APPLICATIONS, 39(3), 3031-3036 [10.1016/j.eswa.2011.08.165].

Matrix representation in pattern classification

LUMINI, ALESSANDRA
2012

Abstract

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.
2012
L. Nanni, S. Brahnam, A. Lumini (2012). Matrix representation in pattern classification. EXPERT SYSTEMS WITH APPLICATIONS, 39(3), 3031-3036 [10.1016/j.eswa.2011.08.165].
L. Nanni; S. Brahnam; A. Lumini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/117932
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