Image Retrieval using MBTC & Gabor Feature Fusion
A Colour & Texture based CBIR System
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Content Based Image Retrieval means that the search makes use of the contents of the images themselves, rather than relying on human input 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. In this book we have proposed a CBIR system which is implemented with the help of combination of features. Block Truncation Coding (BTC) is mainly used for image compression. The proposed method is a modification in original Block Truncation Coding called...
Content Based Image Retrieval means that the search makes use of the contents of the images themselves, rather than relying on human input 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. In this book we have proposed a CBIR system which is implemented with the help of combination of features. Block Truncation Coding (BTC) is mainly used for image compression. The proposed method is a modification in original Block Truncation Coding called as Modified BTC (MBTC) for content based image retrieval system. Texture features are found by calculating the standard deviation of the Gabor filtered image. Gabor Filters & 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 of features. The proposed method is tested thoroughly and to assess the retrieval effectiveness precision and recall as statistical comparison parameters for the MBTC and Gabor Filter based feature vectors are used.
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