A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

Content-based video retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based video retrieval, feature combination plays a key role. As a result content-based retrieval of all different type video data turns out to be a challenging and vigorous problem. This paper presents an effective content based video retrieval system, which recognizes and retrieves videos with three different types of visual effects. The raw video information is divided into shots and also the object feature, movement feature and also the occlusion options are extracted from these shots and also the feature library is used for the storage method of those options. Advanced on, the Kullback-Leibler distance is computed among the options of the feature library and also the options of the question clip that's extracted within the similar manner. The results show that it is possible to improve a system for content-based video retrieval by using Kullback-Leibler distance model, which takes careful consideration of the structure and distribution of visual features. Hence the final results with the aid of the Kullback-Leibler distance the similar videos are extracted from the collection of videos based on the given query video clip in an effective manner.