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Multi-Modal Ontology that Enhances Visual Content Retrieval System

Stefan Poslad 1 and Kraisak Kesorn 2
1. School of Electrical and Electronic Engineering and Computer Science, Queen Mary University of London, E1 4NS, United Kingdom
2. Computer Science and Information Technology Department, Science Faculty, Naresuan University, Phitsanulok, 65000, Thailand
Abstract—This paper presents an ontology-based system for multi-modal image annotation. A novel technique is proposed to represent the semantics of visual content by restructuring visual word vectors to an ontology model from computing a distance between the visual word features and concept features. The second index relies on textual description which is processed to extract and recognise concepts, properties, or instances in an ontology. The two indexes are unified in to a single indexing model used to enhance the image retrieval efficiency. As a result, it is possible to retrieve images with a query using words that do not appear in the caption. The constructed KB was evaluated how well it fits that knowledge domain regarding to its relevance for the application. The results show that the metadata in the presented KB could be exploited efficiently and, thus, it enhances the retrieval performance.

Index Terms—multimodal information, knowledge base, ontology model, image retrieval system

Cite: Stefan Poslad and Kraisak Kesorn, "Multi-Modal Ontology that Enhances Visual Content Retrieval System," International Journal of Electrical Energy, Vol. 1, No. 4, pp. 284-290, December 2013. doi: 10.12720/ijoee.1.4.284-290
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