An Effective Shape Descriptor for the Retrieval of Natural Image Collections

In this work, we present a modified version of the generic Fourier descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduced the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise tend to contaminate the shape feature description. We find that the proposed techniques not only improve overall retrieval in terms of shape, but more importantly, provide a more accurate similarity ranking measure of retrieved results, demonstrating the need for greater consideration for dominant internal and external shape details. A feature database combining color moments, color histograms, Gabor wavelet and shape features is applied in our image retrieval system. Relevance feedback has also been considered, bridging the gap between the high level concepts and the low level visual features. The experimental results indicate that dynamically updating weights associated with feature components by users´ feedback greatly improves retrieval performance