Medical Video Mining for Efficient Database Indexing, Management and Access.pdfVIP

Medical Video Mining for Efficient Database Indexing, Management and Access Xingquan Zhua, Walid G. Arefa, Jianping Fanb, Ann C. Catlina, Ahmed K. Elmagarmidc aDept. of Computer Science, Purdue University, W. Lafayette, IN, USA bDept. of Computer Science, University of North Carolina at Charlotte, NC, USA cHewlett Packard, Palo Alto, CA, USA {zhuxq, aref, acc}@; jfan@; ahmed_elmagarmid@ Abstract1 To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms. 1. Introduction As a result of decreased costs for storage devices, increased network bandwidth, and improved compression techniques, digital videos are more accessible than ever. To help users find and retrieve relevant video more effectively and to facilitate new and better ways of entertainment, advanced technologies must be developed for indexing, filtering, searching, and mining the vast amount of video now available on the web. While numerous papers have appeared on data mining [1-6], few deal wi