Video Retrieval
Video retrieval at TRECVID, from shot boundary detection and concept detection to search.
Back in college, I worked on our team’s entry to TRECVID 2007 (Yuan et al., 2007), an annual benchmark for content-based video retrieval run by the National Institute of Standards and Technology (NIST). The system covered three tasks. For shot boundary detection, it combined block-based color histograms with a motion detector that suppressed false alarms from camera and object motion, followed by a SIFT-based post-processing step. For high-level feature extraction (i.e., concept detection), it learned concept distributions over image regions with multi-label, multi-feature learning, added shape and spatial features, and fused scores both within and across concepts. For video search, it supported both automatic and interactive retrieval.