A Robust Image Retrieval System Using Weighted Composition of Features
A robust, flexible and effective image retrieval system using weighted combination of image retrieval features, is proposed. The proposed method properties such as, shape and spatial features are quite simple to derive and effective, and can be extracted in real time. The system is comprehensive because it incorporates Gabor filters of different grid sizes and flexible because the feature weights can be adjusted to achieve retrieval refinement according to userÃ¢Â€ÂŸs need and robust because the systemÃ¢Â€ÂŸs algorithm is applicable to retrieval in all kinds of image database. In CBIR systems the common method of improving retrieval performance is by weighting the feature vectors. In this paper a new and reliable method of improving retrieval performance, and which complement feature weighting is proposed. Based on results obtained from this paper, we hereby state that the key to a breakthrough in current research in semantic image retrieval lies in the use of Gabor texture feature. Its benefits of Fourier as well as local analysis of images enable analysis of gradual changes of texture and texture variations which are essential properties of real-world scenes.