In this paper, analysis of the feature selection for scale invariance texture image retrieval using fuzzy logic classifier and wavelet and co-occurrence matrix based feature is carried out. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Cooccurrence 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. Best and Worst case analysis is done for each of the feature set and texture image size. Also the minimum number of features required for maximum average success rate is obtained. This study shows that for samples taken from 256x256 texture size, excellent success rate is achieved for Wavelet Statistical Features (WSF) as well as Wavelet Co-occurrence Features (WCF). Also WSF perform better for 128x128 and 256x256 texture image. For both the types of features performance degrades in case of 512x512 texture image. Worst case analysis shows that energy feature WSF and 8-features group WCF performs excellently.

Over the years passed has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Similarly, digital image retrieval has expanded in many directions that are resulting into explosion in the volume of image data required to be organized. This paper presents a framework for image retrieval based on chain code and auto regression that helps to achieve higher retrieval efficiency. In this paper, we discuss about the key contributions of the methodology that is followed while performing experiment for image retrieval based on chain code and auto regression. Here comparative study of results and also efficiency of both these image retrieval techniques are discussed which are obtained while experimentation.

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