Semantic Similarity Based Video Retrieval

In this paper, we propose semantic similarity measure to overcome semantic gap in video retrieval. In particularly, our method is feature selection for the video ontology construction. Video ontology is aimed at bridging of the gap between the semantic nature of user queries and raw video contents using scene keyword. Moreover, results of semantic retrieval show not only the concept of topic keyword but also a sub-concept of the topic keyword using semantic query extension. Through this process, recall is likely to provide high accuracy results in our method. The experiments compared with keyframe-based indexing have demonstrated that this proposed scene-based indexing presents better results in several kinds of videos.