Content Based Image Retreival Using Fusion of Gabor Magnitude and Modified Block Truncation Coding

Content-based means that the search makes use of the contents of the images themselves, rather than relying on human inputted metadata such as captions or keywords. By content-based techniques, a user can specify contents of interest in a query. The contents may be colors, textures, shapes, or the spatial layout of target images. We propose a CBIR system which is implemented with the help of combination of features. BTC is mainly used for image compression. The proposed method is a modification in original block truncation coding (BTC) for content based image retrieval system. Texture features are found by calculating the standard deviation of the Gabor filtered image. Gabor Filter & Modified Block Truncation Coding based feature vector is extracted, then compared with corresponding feature vector of images stored in the database. Images are retrieved based on the similarities features. The proposed method is tested on a database consisting of 1000 color images for test. All images in database are ranked according to their similarity to query image. To assess the retrieval effectiveness precision and recall as statistical comparison parameters for the MBTC and Gabor Filter based feature vector is used.