The Research On Key Techniques Of Content-Based Video Retrieval

With the rapid development of the multimedia technology and network technology, the access and dissemination of digital video has became more easily.The video has became one of the main carrier of information dissemination. In the days of highly inflated video information, the attendant problem is efficient retrieval and browsing to the massive video. The traditional way of video retrieval searches in the way that adds the text identifier using manual methods. The workload of this method is large and the efficiency is low. Also the performance of this methods is affected by the subjective factor, so it can't meet the actual needs.The Content-Based Video Retrieval which get the content of the video employing the method of processing, analysis and understanding to the video from low to high-level by computer and searches video accordance with it has became one of hot issue in the field of multimedia information retrieval.This paper summarized and analysised the the theoretical framework and the study status of video retrieval firstly,then conducted a in-depth study and exploration to some key Techniques in this field, including shot segmentation,key frame extraction and shot clustering. The shot segmentation is the first step to the video retrieval.On the basis of concluding the existing algorithm, the paper did some research into shot segmentation based on mutual information. The paper designed and implemented a Cut Transition algorithm based on dual-sliding window, which calculate the Mutual information among frames to determine the similitude of two frames and use the dual-sliding window method to find the local extremum of neighbor frame mutual information in order to locate the boundary of cut shots.The paper proposed a mutual information Cut Transition detecting algorithm based on image block against the interference of the movement and flash in the detection of Cut Transition.The mutual information is used as the criteria for evaluation of differences between frames.The algorithm partition the image first and then calculate the mutual information of the corresponding sub-image between the adjacent image.Then the algorithm conduct a inverse proportion transformation to the value of mutual information and do a accumulation to all values in the whole image.At last the algorithm use adaptive threshold method to identify the local extreme of the difference between frames to find the boundary of cut shots.The paper research and achieve a shot Gradual Transition algorithm which the difference of different frame spacing of non-neighbor frame mutual information is used to inspect the boundary of gradual transition shots. Experiment results show that the algorithm is simple,clear indicators and achieve a high recall rate and accuracy.The key frame extraction is the key step of content-based video retrieval. The paper first conduct a research on the principle and the main methods of key frame extraction technology and thenr made a introduction of mutual information to the key frame extraction and advised a mutual information based key frame extraction algorithm.The algorithm calculate the standard deviation of the difference between frames aim at the variety of mutual information inside the shot to determine the similarity of consecutive frames from which extract a key frame. The experiment results showe that the use of this method can accurately reflect the contents of the shots and get a real sense of the key frame better.The shot clustering plays a very important role in Content-Based Video Retrieval system as a bridge from the low-level characteristics of video content to the the high abstract of video content.The paper first do some research to the rinciples and characteristics of clustering analysis and analysised the major algorithms in the areas simplely. After that the paper suggested a shot cluster algorithm based the color characteristics of the key frame.The algorithm use an improved K-means clustering algorithm for shot clustering according as color characteristics of the key frame and optimize the clustering results. Experimental results show that the method achieved the high accuracy rate and efficiency, increase the stability of the Cluster results, obtained the satisfactory results.