An effective video search re-ranking for content based video retrieval

Content-Based searching and retrieval of video data has become a challenging and important issue among the search engines. Mostly videos were retrieved based on keyword from a large collection of videos. Given a text query by users, the system then returns a series of approximately relevant video shots by matching the input text with the text documents associated with the video shots. Users are usually interested in the top ranked portion of returned search results and therefore it is crucial for search engines to achieve accuracy on the search results. In this paper content based video searching re-ranking and retrieval framework is proposed to improve the efficiency and accuracy of the search engines. Here input is given as a video, where the video is converted into frames and key frames are extracted. Motion estimation, Colour, edge and texture features are extracted for the key frames. Initial ranking on the search results is done for distinct features. Then features are Fused and finally re-ranked to form a unified accurate search result.