On Size Invariance Texture Image Retrieval using Fuzzy Logic and Wavelet based Features

摘要

In this paper, analysis of the feature selection for size invariance texture image retrieval using fuzzy logic classifier and wavelet based feature is carried out. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Co-occurrence matrix. Energy and Standard Deviation are obtained from each sub-band of DWT coefficients up to fifth level of decomposition and eight features are extracted from co-occurrence matrix of whole image and each sub-band of first level DWT decomposition. The different size samples of texture image are undertaken. The suitability of features extracted is analyzed using a fuzzy logic classifier. The performance is measured in terms of Success Rate. Worst case analysis is done for each of the feature set and sample size. Also the minimum number of features required for maximum average success rate is obtained. This study shows that 256×256 sample size gives success rate of above 95% in worst case analysis, when energy and standard deviation features taken together, energy feature taken alone, and for 8 co-occurrence features. Standard deviation feature alone and group of five co-occurrence features fail achieve better performance.