Features Composition for Proficient and Real Time Retrieval in Cbir System

In this paper, Focus is on texture as primary feature. Shape and spatial information were secondary features. Texture features derived from nine grid sizes of independent and different Gabor filter banks were incorporated into the CBIR system by taking advantage of the fact that each grid size of filter is suited to capture particular set of localized frequency-images in diverse database. This design enable the Gabor filter to optimally cover the frequency space, and gives the system the artificial intelligence to ‘scroll’ locally and globally through the database and retrieve images based on high level features. It is shown that Gabor filters can replay their efficient texture feature extraction in pure texture images, in complex and real-world images, because these images, though constituted by constant grey levels, the various constant grey levels within the global image constitute texture that can be captured by the tunable characteristics of Gabor filters. The measured results certify the robustness of proposed method.