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TEXTURE CLASSIFICATION USING WAVELET TRANSFORM AND DYNAMIC FEATURE SELECTION

 

TITLE TEXTURE CLASSIFICATION USING WAVELET TRANSFORM AND DYNAMIC FEATURE SELECTION
AUTHOR SURACHAI DIDSAWAT
DEGREE MASTER OF SCIENCE PROGRAMME IN COMPUTER SCIENCE
FACULTY FACULTY OF SCIENCE
ADVISOR SUKANYA PHONGSUPHAP
CO-ADVISOR SUPATANA AUETHAVEKIAT
 
ABSTRACT
Wavelet transform is a multi-scale technical analysis which is popular for image analysis and it has been applied to many aspects of it. The method is to divide the original image into multi-scales through the convolution with the mother wavelet, where the translation and scaling can acquire subimages. Then, the texture feature will be extracted from subimages, which can be used to describe the image in more detail. Previous research which has discriminated texture image through wavelet transform and which only used a small number of Brodatz textures. It had an accuracy rate of more than 90%. However, in trials of the entire 112 texture categories, it was found that the accuracy rate decreased to approximately 70%. Because of this, this research proposed the wavelet transform and dynamic feature selection, which chooses the feature for the adoption on the automatic discrimination of the texture category through the vote from each feature. There are 5 features implemented in this research, i.e. norm-1(average energy), standard deviation, average residual, entropy (log energy) and maximum probability. This research used two experimental methods on Brodatz textures. The first experiment was tried on 20 texture categories. The trial with the tree structure wavelet transform and the dynamic feature selection acquired an accuracy rate of 97.9%. The second experiment was tried on 112 texture categories. The outcome of the trial on the pyramid wavelet transform and tree structure wavelet transform had accuracy rates of 77.7% and 74% respectively. However, upon adopting the tree structure wavelet transform and dynamic feature transform the accuracy rate increased to 87%. It is apparent that the tree structure wavelet transform and dynamic feature selection can improve the classification result.
KEYWORD TEXTURE CLASSIFICATION, WAVELET TRANSFORM, MULTI-SCALE, IMAGE ANALYSIS, FEATURE SELECTION

 

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