Content-based image retrieval using Generic Fourier Descriptor and Gabor Filters

Content-based image retrieval (CBIR) is an important research area with applications to a large amount of image databases and multimedia information. In CBIR, image is described by several low-level image features, such as color, texture, shape or their combination. The focus of this paper is on the problem of shape and texture feature extraction and representation for CBIR.; Shape is one of the most important features because it is very important to human perception. Two shape representation methods, region based shape descriptors and contour based shape descriptors have been studied. A new shape descriptor, Generic Fourier Descriptor (GFD) has been discussed and evaluated with existing shape descriptors. Texture is a global feature that could be used to associate related shapes. Gabor filters (GF) has been selected for texture feature extraction. We apply Generic Fourier Descriptor (GFD) for shape feature extraction and Gabor Filters (GF) for texture feature extraction on three different databases, and we successfully combined GFD and GF together for shape and texture feature extraction. Experimental results show that the proposed GFD+GF is robust to all the test databases with best retrieval rate.