Florida International University and University of Miami TRECVID 2008 - High Level Feature Extraction

This paper describes the FIU-UM group TRECVID 2008 high level feature extraction task submission. We have used a correlation based video semantic concept detection system for this task submission. This system first extracts shot based low-level audiovisual features from the raw data source (audio and video files). The resulting numerical feature set is then discretized. Multiple correspondence analysis (MCA) is then used to explore the correlation between items, which are the feature-value pairs generated by the discretization process, and the different concepts. This process generates both positive and negative rules. During the classification process each instance (shot) is tested against each rule. The score for each instance determines the final classification. We have conducted two runs using two different predetermined values as the score threshold for classification:

• Type: Conference
• Year: 2008
• Where: TRECVID
• Authors: Guy Ravitz, Lin Lin, Mei-Ling Shyu, Michael Armella, Shu-Ching Chen
• Keywords: Development Data, Final Classification, Internet Technology, Partial Trecvid2007 Test, TRECVID 2008