Shape Indexing and Semantic Image Retrieval Based on Ontological Descriptions

This paper presents some hybrid approaches for visual information retrieval that combine image low-level feature analysis with semantic descriptors of image content. The aim of this proposal is to improve retrieval process by reducing nonsense results to user query. In the proposed approach user may submit textual queries, which are converted to image characteristics providing in this way searching, indexing, interpretation, and retrieval. In the case of visual query, both an image and sketch may be used. Approaches for image interpretation and retrieval are applied to color filtering, shape indexing and semantic. In order to assess the proposed approaches, some systems for image retrieval have been designed. The simplest system uses color region arrangement and neural network or wavelet based classifiers. Then this system has been improved using shape analysis with its indexing by ontological descriptions. For shape matching two proposed approaches are used such as star field or two-segment turning functions, which are invariant to spatial deformation of objects in image. The ontological annotations of objects in image provide machine-understandable semantics. The evolution of the proposed approaches and improvement of retrieval process are described in this paper. Four designed systems are assessed: RetNew, IRWC, Butterfly, and IRONS tested on standard COIL-100 and CE-Shape-1 image collections. The obtained results will allow to develop novel methods for solving efficient image retrieval processes.