Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback
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Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback Vasileios Mezaris Information Processing Laboratory, Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece Centre for Research and Technology Hellas (CERTH), Informatics and Telematics Institute (ITI), 57001 Thessaloniki, Greece Email: [email protected]
Ioannis Kompatsiaris Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece Centre for Research and Technology Hellas (CERTH), Informatics and Telematics Institute (ITI), 57001 Thessaloniki, Greece Email: [email protected]
Michael G. Strintzis Information Processing Laboratory, Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece Centre for Research and Technology Hellas (CERTH), Informatics and Telematics Institute (ITI), 57001 Thessaloniki, Greece Email: [email protected] Received 31 January 2003; Revised 3 September 2003 An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) and their relations in a humancentered fashion. When querying for a specific semantic object (or objects), the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach. Keywords and phrases: image retrieval, image databases, image segmentation, ontology, relevance feedback, support vector machines.
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INTRODUCTION
In recent years, the accelerated growth of digital media collections and in particular still image collections, both proprietary and on the web, has established the need for the development of human-centered tools for the efficient access and retrieval of visual information. As the amount of information available in the form of still images continuously increases, the necessity of efficient methods for the retrieval of the visual information becomes evident [1]. Additionally, the continuously increasing number of p
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