VIREO/DVMM at TRECVID 2009: High-level feature extraction, automatic video search, and content-based copy detection

This paper presents overview and comparative analysis of our systems designed for 3 TRECVID 2009 tasks: high-level feature extraction, automatic search, and content-based copy detection. High-Level Feature Extraction (HLFE): Our main focus for the HLFE task is on the study of a new method named domain adaptive semantic diffusion (DASD) [1], which exploits semantic context (concept relationship) while also considers the domain-shift-of-context to improve concept detection accuracy. We apply our TRECVID 2008 HLFE system [2] to construct baseline detectors for the 20 evaluated concepts, where both local and global features are explored. Evaluation results show that our 2008 system is still able to produce strong performance (Run 5: MAP=0.156). Over the 20 strong baseline detectors, DASD consistently improves 17 concepts using a set of 300+ relatively much weaker detectors (from VIREO-374 [3]) as contexts (Run 1-4). Our 6 submitted runs are summarized below: - A_vireo.dasd20scorelinear_1: DASD over a baseline using linear weighted fusion of local and global features. Concept affinity estimation method is the same to Run 3. - A_vireo.dasd20fcs_2: DASD over Run 5; using ground-truth annotations and Flickr context to estimate concept affinity. - A_vireo.dasd20score_3: DASD over Run 5; using ground-truth annotations and detection score to estimate concept affinity. - A_vireo.dasd10_4: DASD over Run 5; using ground-truth annotations to estimate concept affinity (only applied for 10 concepts). - A_vireo.localglobal_5: average fusion of local and global features. - A_vireo.localalone_6: local feature alone - multiple detectors and spatial partitions.