Efficient and Cost-effective Techniques for Browsing and Indexing Large Video Databases Jung-Hwan Oh and Kien A. Hua   View Paper (PDF)   Return to Research Sessions Abstract We present in this paper a fully automatic content-based approach to organizing and indexing video data. Our methodology involves three steps: Step 1: We segment each video into shots using a Camera-Tracking technique. This process also extracts the feature vector for each shot, which consists of two statistical variances VarBA and VarOA. These values capture how much things are changing in the background and foreground areas of the video shot. Step 2: For each video, We apply a fully automatic method to build a browsing hierarchy using the shots identified in Step 1. Step 3: Using the Var BA and VarOA values obtained in Step 1, we build an index table to support a variance-based video similarity model. That is, video scenes/shots are retrieved based on given values of VarBA and VarOA. 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IEEE Computer 32(2) : 66-73(1999) BIBTEX @inproceedings{DBLP:conf/sigmod/OhHP00,   author    = {Jung-Hwan Oh and                 Kien A. Hua},    editor    = {Weidong Chen and                 Jeffrey F. Naughton and                 Philip A. Bernstein},    title     = {Efficient and Cost-effective Techniques for Browsing and Indexing                 Large Video Databases},    booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on                 Management of Data, May 16-18, 2000, Dallas, Texas, USA},    journal   = {SIGMOD Record},    publisher = {ACM},    volume    = {29},    number    = {2},    year      = {2000},    isbn      = {1-58113-218-2},    pages     = {415-426},    crossref  = {DBLP:conf/sigmod/2000},    bibsource = {DBLP, http://dblp.uni-trier.de} } }, DiSC'01 Copyright ©2002 ACM Inc.