A Novel Shape and Ontological Indexing for VIR Systems

This paper presents a novel hybrid method for visual information retrieval (VIR) that combines shape analysis of objects in image with their automatic indexing by textual descriptions. The principal goal of proposed method is the applying semantic Web approaches for visual information description in systems which use the low-level image characteristics. In the proposed method the user-oriented textual queries are converted to image characteristics which are used for visual information seeking and matching analysis. A decision about similarity between a retrieved image and user queries is taken by computing the shape convergence star field or two-segment turning functions combining them with matching of ontological annotations of objects in image providing in this way the machine-understandable semantics. For analysis of proposed method the image retrieval IRONS (Image Retrieval by Ontological Description of Shapes) system has been designed and evaluated in some specific image-restricted domains.