Research On Near Duplicate Video Clip Detection Technology Supporting Cartoon Video Analysis

With the fast development of video technology and the rapid spread of internetmedia，the cartoon video which is an important embranchment of video data is nowattracting more and more attention while the cartoon industry is broadening continually.Howerver, the abundant cartoon video resource makes users difficult to find theirneeded information in video dataset. As an important composing of cartoon videomaterial, cartoon video clips often bear tremendous repeated information and have ahigh reuse rate, which make it becomes the main research object in video domain.Recently, as the amount of cartoon video clip is increasing exponentially, the nearduplicate cartoon video clip matching technology has become one of the hotspots inmultimedia retrieval domian. How to obtain an effective matching technology of nearduplicate cartoon video clips, and use it to realize the automaticlly analyse of cartoonvideo has become one of problems which are needed to solve urgently in video domain.The near duplicate video clip matching technology is affected by two factors:veracity and velocity. Because of the large video information among different structurelevels and semantic levels of video clip, the computation complicacy is huge too. Thus,how to implement the tradeoff between veracity and velocity has always been thediffculty in the near duplicate video clip matching research. This thesis explores thetopic on near cartoon video clip matching technology, a num of correlative technologiesand methods are proposed. The target is to support the video clips analyse and helpusers to obtain needed cartoon video information.To realize those targets, the following key technologies are researched fromsystem’s point: cartoon image feature extraction and matching, near duplicate cartoonvideo clip matching, caontent-based online cartoon video clip detection, the annoatationof cartoon video clip and relevancy among near duplicate cartoon video clips. In detail,the main contributions of this thesis can be concluded as follows:Firstly, a color combined visual feature extraction and matching method of cartoonimage is proposed. Aimed at the cartoon image’s features which are distinc from naturalimage, through the embeding of the relevancy feature of image’s color distribution intothe global color descriptor, the matching accuracy is improved. Meanwhile, Aimed atthe problem of missing color information in local feature extraction method, the colorinvarance is used as the input of local feature extraction, so the compoment details ofcartoon image is well described. Finally, the weighted fusing methods of the cartoonimage’s global and local feature are researched in order to realize the advantages’complementation between the two features.Secondly, the matching methods of near duplicate cartoon video clip on differentstructure levels and semantic levels are proposed. The research is done from the bottom feature level and middle logical level. Firstly, aimed at the bottom level, thekeyframe-based bag of word method and edit distance method are improved by usinglanguage model and extended edit distance to describe the visual feature and sequencefeature of keyframe respectively. Secondly, aimed at the middle level, the keyframebased sequence net method and unit based video track method are proposed. Theanterior one fuses the visual feature and sequence feature by building the net whichsolve the problem of partially alignment. Based on the disciption parameter of videounit feature, the latter one realizes the best alignment by employing the optimizationmatching technology of graph theory in order to achieve the traderoff between veracityand velocity. Finally, the similarity fusing approach among different levels of cartoonvideo clip matching is researched, during which the diverse application situation andtask demands can be satisfied by adjusting the weighted coefficient.Thirdly, methods of cartoon video clip supporting analyse technology are proposed.Firstly, a content-based online near duplicate video clip detection method is realized,which employs an improved index structure to speed up the detection rate and proposesa relevancy graph based resorting method in order to resort the detection result;Secondly, an automatical annotation method of cartoon video clip which empoly therandom walk approach is proposed to solve the problem of overfull wrong labels andconsummate the clip’s semantic information; Lastly, kinds of visualization structuresare proposed to present the relevancy among near duplicate cartoon clips, which are:class relevancy, feature relevancy and evolution process, in order to provide a novelthought for mining the deep information of cartoon video clip.Lastly, a system for supporting analyse cartoon video clip is designed andimplemented. The design idea and each functional module of NCLIPs system aredescribed in detail, and the implementation of prototype system is also presented. TheNCLIPs system use the technologies of this thesis as the support to implement thecontent-based analyse, retrieval and bardian organazation of cartoon video clips.As a general, the supporting analyse of cartoon video clip is realized based on nearduplicate video clip matching. From the point of pratical effect, the proposed nearduplicate cartoon video clip matching method has a good veracity and velocity. Thistechnology can be use as a technical support for videos analyse, retrieval andorganazation. From the point of research meaning, achievements of this thesis providean effective method to obtain the cartoon video information. The research productionhas an important meaning in theory and pratical use.