Wavelet packet neural networks for texture classification |
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| Affiliation: | 1. Fırat University, Department of Electronic and Computer Science, 23119 Elazig, Turkey;2. Fırat University, Department of Electric–Electronic Engineering, 23119 Elazig, Turkey;1. Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia;2. Department of Materials Science and Engineering, Monash University, Melbourne, VIC 3800, Australia;1. School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China;2. Tianjin Key Laboratory of Advanced Joining Technology, Tianjin 300072, China;1. Laboratoire d''Etude des Microstructures et de Mécanique des Matériaux (LEM3), UMR 7239, CNRS / Université de Lorraine, F-57045 Metz, France;2. Laboratory of Excellence on Design of Alloy Metals for low-mAss Structures (DAMAS), Université de Lorraine, Metz, France;3. Donetsk Institute for Physics and Engineering named after O.O. Galkin, National Academy of Sciences of Ukraine, 36 Vernadsky St., Kyiv 03142, Ukraine;1. Royal North Shore Hospital, Sydney, NSW, Australia;2. RACS Trainees Association, RACS, Melbourne, Australia;3. Division of Surgery, University of Sydney, Sydney, Australia;4. Department of Surgery, University of Western Sydney, Sydney, Australia;5. Department of Surgery, University of Auckland, Auckland, New Zealand |
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| Abstract: | Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. This paper describes the usage of wavelet packet neural networks (WPNN) for texture classification problem. The proposed schema composed of a wavelet packet feature extractor and a multi-layer perceptron classifier. Entropy and energy features are integrated wavelet feature extractor. The performed experimental studies show the effectiveness of the WPNN structure. The overall success rate is about 95%. |
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