Efficient and Robust Retrieval by Shape Content through Curvature Scale Space

We introduce a very fast and reliable method for shape similarity retrieval in large image databases which is robust with respect to noise, scale and orientation changes of the objects. The maxima of curvature zero crossing contours of Curvature Scale Space (CSS) image are used to represent the shapes of object boundary contours. While a complex boundary is represented by about ve pairs of integer values, an eeective indexing method based on the aspect ratio of the CSS image , eccentricity and circularity is used to narrow down the range of searching. Since the matching algorithm has been designed to use global information, it is sensitive to major occlusion, but some minor occlusion will not cause any problems. We have tested and evaluated our method on a prototype database of 450 images of marine animals with a vast variety of shapes with very good results. The method can either be used in real applications or produce a reliable shape description for more complicated images when other features such as color and texture should also be considered. Since shape similarity is a subjective issue, in order to evaluate the method, we asked a number of volunteers to perform similarity retrieval based on shape on a randomly selected small database. We then compared the results of this experiment to the outputs of our system to the same queries and on the same database. The comparison indicated a promising performance of the system.