Efficient And Cost-Effective Techniques For Browsing And Indexing Large Video Databases
Shot detection; Video browsing; Video indexing; Video retrieval; Video similarity model
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 VarBA 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. The above three inter-related techniques offer an integrated framework for modeling, browsing, and searching large video databases. Our experimental results indicate that they have many advantages over existing methods.
SIGMOD Record (ACM Special Interest Group on Management of Data)
Number of Pages
Source API URL
Oh, Jung Hwan and Hua, Kien A., "Efficient And Cost-Effective Techniques For Browsing And Indexing Large Video Databases" (2000). Scopus Export 2000s. 1041.