Color and Shape Index for Region-Based Image Retrieval

Most CBIR systems use low-level visual features for representation and retrieval of images. Generally such methods suffer from the problems of high-dimensionality leading to more computational time and inefficient indexing and retrieval performance. This paper focuses on a low-dimensional color and shape based indexing technique for achieving efficient and effective retrieval performance. We propose a combined index using color and shape features. A new shape similarity measure is proposed which is shown to be more effective. Images are indexed by dominant color regions and similar images form an image cluster stored in a hash structure. Each region within an image is further indexed by a region-based shape index. The shape index is invariant to translation, rotation and scaling. A JAVA based query engine supporting query-byexample is built to retrieve images by color and shape. The retrieval performance is studied and compared with a region-based shape indexing scheme.