cient and Cost - e ective Techniques for Browsing and Indexing Large Video Databases

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 V ar and V ar. 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 identi ed in Step 1. Step 3: Using the V ar and V ar 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 V ar BA and V ar. The above three inter-related techniques o er an integrated framework for modeling, browsing, and searching large video databases. Our experimental results indicate that they have many advantages over existing methods.