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.