Edge-Based Spatial Descriptor for Content-Based Image Retrieval

Abstract

The need for tools that effectively filter and efficiently search through a large amount of visual data is on the increase due to the rapid growth of multimedia information. Towards this goal, we propose a novel approach for image retrieval based on edge structural features using edge correlogram and color coherence vector. After color vector angle is applied to an image in the pre-processing stage, it is divided into two parts, as either smooth or edge pixels by the pixel classification. For the smooth pixels, the global color distribution of pixels is extracted by color coherence vector, incorporating spatial information into the proposed color descriptor. Meanwhile, for the edge pixels, the distribution of the gray pairs at an edge is extracted by edge correlogram. As the proposed method has both information for the local spatial correlation and information of global distribution of colors, it can be used to reduce the effect of the significant change in appearance and shape of objects. From the image representation based on edge structural features, the proposed algorithm provides a concise and flexible description even for the image with the complicated scenes. Experimental evidence shows that our algorithm outperforms the recent histogram refinement methods for image indexing and retrieval.