Image and Video Analysis

Image Analysis Using Domain Knowledge and Visual Context

13th International Conference on Systems, Signals and Image Processing, Budapest, Hungary, September 2006.

Tackling the problems of automatic object recognition and/or scene classification with generic algorithms is not producing efficient and reliable results in the field of image analysis. Restricting the problem to a specific domain is a common approach to cope with this, still unresolved, issue. In this paper we propose a methodology to improve the results of image analysis, based on available contextual information derived from the popular sports domain. Our research efforts include application of a knowledge-assisted image analysis algorithm that utilizes an ontology infrastructure to handle knowledge and MPEG-7 visual descriptors for region labeling. A novel ontological representation for context is introduced, combining fuzziness with Semantic Web characteristics, such as RDF. Initial region labeling analysis results are then being re-adjusted appropriately according to a confidence value readjustment algorithm, by means of fine-tuning the degrees of confidence of each detected region label. In this process contextual knowledge in the form of domain-specific semantic concepts and relations is utilized. Performance of the overall methodology is demonstrated through its application on a real-life still image dataset derived from the tennis sub-domain.

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