Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. Semantics based methods have been used to describe images according to their high level features. In this paper, we performed experiments to identify the failure of existing semantics-based methods to retrieve images in a particular semantic category. We have proposed a new semantic category to describe the intra-region color feature. The proposed semantic category complements the existing high level descriptions. Experimental results confirm the effectiveness of the proposed method
In this paper, we have proposed a method for 2D image retrieval based on object shapes. The method relies on transforming the 2D images into 3D space based on distance transform. Spherical harmonics are obtained for the 3D data and used as descriptors for the underlying 2D images. The proposed method is compared against two existing methods which use spherical harmonics for shape based retrieval of images. MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the performance of the proposed descriptors is significantly better than other methods in the same category.