Study Of Content-Based Video Clip Retrieval

Rapid growth of video resources leads to an urgent demand for effectively retrievinginteresting video clip from huge amounts of video data in recent years. As text retrieval,video retrieval is also expected that a retrieval system can automatically return results inaccord with query demand when users submit query video content. Content-based videoretrieval techniques have emerged as the times requires, which combine image processing,pattern recognition, computer vision and image understanding, etc. And it has wideapplication foreground.This paper mainly presents content-based clip retrieval methods of three video genres,commercial video, racquet sports video and news video.A new commercial video clip retrieval method is presented, in which the similar shotset of shots of query clip is defined to compute the number of one-one similar shotsbetween two video clips, on the basis of this, the definition of matching function of twovideo clips is given. Afterwards, similar commercial clips are automatically segmentedfrom continuous video databases using sliding shot window according to their matchingdegrees. Furthermore, the similar shot sets are mapped into one similar shot matrix tocompute various factors for similarity ranking of the similar clips by characters of thematrix. Experimental results showed that the proposed method could effectively andefficiently retrieve and rank similar video clips.This paper presents a new audiovisual integration scheme for retrieving rally highlightfrom racquet sports video. Motion features and dominant color are applied to classify shotsinto two classes, global view shots and non-global view shots. At the same time, importantauditory features including both ball hitting and applause are detected by using SVM.Afterwards, audio features are applied into shot classes for identifying interesting eventswith strong semantic meaning. Finally, rally highlights are retrieved by rally clips replayedand exciting degree model of a rally from racquet sports video.This paper presents a scheme of retrieving news video clips based on low-level visualfeature and high-level semantic information. The semantic information is obtained fromtopic caption text. In the method, news video is first segmented into a series of news storyclips on the basis of topic caption text and silence clip. Moreover, the segmented story clips are annotated by topic caption text. Afterwards, a similarity model of two news clipsis established based on topic caption text and low-level visual features. Furthermore,relevant feedback technique is applied to retrieve similar news story clips, which haverelative topic, or similar vision, or relative topic and similar vision with query news clips.A content-based video clip retrieval platform was designed and implemented tovalidate the performance of the above clip retrieval methods for three video genres.