Edge-Based Spatial Descriptor Using Color Vector Angle for Effective Image Retrieval

Abstract

In this paper 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 in the pre-processing stage, an image is divided into two image parts (high frequency image and low frequency image). In a low frequency image, the global color distribution of smooth pixels is extracted by color coherence vector, and thereby spatial information is incorporated into the proposed color descriptor. Meanwhile, in a high frequency image, the distribution of the gray pairs at an edge is extracted by edge correlogram. Since the proposed algorithm includes the spatial and edge information between colors, it can robustly reduce the effect of the significant change in appearance and shape of objects. The proposed method provides a simple and flexible description for the image with complex scene in terms of structural features from the image contents. Experimental evidence shows that our algorithm outperforms the recent histogram refinement methods for image indexing and retrieval. To index the multi-dimensional feature vectors, we use R*-tree structure.