Matrix representation in pattern classification |
| |
Authors: | Loris Nanni Sheryl Brahnam |
| |
Affiliation: | a Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy b Computer Information Systems, Missouri State University, 901 S. National, Springfield, MO 65804, USA c Department of Electronic, Informatics and Systems (DEIS), Università di Bologna, Via Venezia 52, 47023 Cesena, Italy |
| |
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. |
| |
Keywords: | Pattern classification Texture descriptor Locally ternary patterns Local phase quantization Support vector machines |
本文献已被 ScienceDirect 等数据库收录! |
|