Similarity retrieval of shape images based on database classification

Shape is a key visual feature used to describe image content. This paper develops a novel shape-based similarity retrieval system based on database classification which exploits the contour and interior region of a shape efficiently. In this system, the database of shape images is categorized automatically into 11 classes by a simple contour feature. In query, the contour feature of the input image is used to decide which class the query image belongs to. Then, the possible classes are selected dynamically from the database and to form candidate sets with different priority orders. Then, ART region feature is employed to compare the query with the candidate sets according to the priority order. Instead of using the original contour of a shape image directly, we employ a rough version of the original contour for the classification of shapes. The similarity test results indicate that the proposed method improves retrieval accuracy and speed significantly, as compared to ART.