Content Based Refinement On Video Search Result

With the rapid development in multi-media technology and the easy access to digital record device, the internet is witnessing online video-data explosion. Online video storage, organization and management has became one of the most cutting-edge topic in video domain. Currently, the hottest video search engine in the world, including Google Video, Yahoo Video, Bing Video and Baidu Video, tend to utilize query-by-keyword scenario for video annotation, mining the surrounding text of video data for video search. The main assumption for this scenario is that information retrieval theory in web-page domain can be directly applied to video domain. However, this mechanic faces two disadvantages: a large amount of online video present contradicting content level information against surrounding text; video data is too multi-informative to be described by single text.To overcome the disadvantages of text-based video search technology, the research community is intended to apply content based refinement on video search result. Content based refinement means to refine the ranking list by mining content information through analysis, based on the original text search result. The main reason for content based refinement is to present better user experience and optimize the over-all satisfaction of video search.This desertion conduct video search refinement in three domain: relevance aimed reranking, video quality assessment and copy detection, which contributes to a integrated system for the optimization of text search result:(1) This desertion studies visual reranking via adaptive particle swarm optimization, to upgrade the overall relevance of search result. Compared with traditional reranking methods, this approach models reranking as a swarm intelligence based evolutionary optimization process. The essence of reranking is studied in this desertion.(2) This desertion studies content based video quality assessment, to control the overall quality in the return list of video search. Several key factors which are largely responsible for video visual quality are studied one by one. Different from Full-Reference quality assessment and signal theory based assessment, our approach involves no reference video in the assessing process.(3) To reduce the redundancy in the search result, this desertion studies content based video copy detection, to effectively detect similar videos in the search result. This desertion studies the main categories of video copy on the internet, indicating the detection efficiency is the key for the practical video search refinement scenario. We apply raw matching and refine matching in our 2-level matching implementation. Early-stopping mechanic is adopted in each of the matching process. Experiments indicate our approach confirms detection precision while largely improves detection efficiency.