Interactive Video Retrieval Using Semantic Level Features and Relevant Feedback

Recent years, many literatures presents a lot of work for content-based video retrieval using different set of feature. But, most of the works are concentrated on extracting features low level features. But, the relevant videos can be missed out if the interactive with the users are not considered. Also, the semantic richness is needed further to obtain most relevant videos. In order to handle these challenges, we propose an interactive video retrieval system. The proposed system consists of following steps: 1) Video structure parsing, 2) Video summarization and 3) Video Indexing and Relevance Feedback. At first, input videos are divided into shots using shot detection algorithm. Then, three features such as color, texture and shape are extracted from each frame in video summarization process. Once the video is summarized with the feature set, index table is constructed based on these features to easily match the input query. In matching process, query video is matched with index table using semantic matching distance to obtain relevant video. Finally, in relevance feedback phase, once we obtain relevant video, it is given to identify whether it is relevant for the user. If it is relevant, more videos relevant to that video is given to the user. The evaluation of the proposed system is evaluated in terms of precision, recall and f-measure. Experiments results show that our proposed system is competitive in comparison with standard method published in the literature.