Ontologies for object-based image retrieval

In this paper, a novel approach to image retrieval is presented. The proposed ap-proach employs a time-eÆcient and fully unsupervised segmentation algorithm to divide images into regions. Low-level indexing features for each region are subse-quently extracted. These arithmetic features are automatically mapped to appro-priate qualitative descriptors of the object ontology. The object ontology is also used for describing semantic objects (keywords). By querying for images conform-ing to the qualitative description of the desired object, clearly irrelevant image regions are rejected; following that, a relevance feedback mechanism, based on neural networks, is invoked to rank the remaining, potentially relevant regions and produce the nal query results. The proposed approach bridges the gap between keyword-based approaches, which assume the existence of rich image captions, and query-by-example approaches, which assume that the user queries for images similar to one that already is at his disposal. 1