Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Color, texture and shape information have been the primitive image descriptors in content-based image retrieval systems. This paper presents a fast and efficient image indexing and search system based on color and texture features. The color features are represented by combines 2-D histogram and statistical moments and texture features are represented by 2-D Localized SDFT that uses the Gaussian kernel to offer the spatial localization ability. 2-D SDFT is expected to provide more useful information. It is observed that color features in combination with the texture features derived from the brightness component provide approximately similar results when color features are combined with the texture features using all three components of color, but with much less processing time. The detailed experimental analysis is carried out using precision and recall on two datasets: Corel-DB, Coil-100. The time analysis is also performed to compare processing speeds of the proposed method with the existing similar best.
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