Columbia University’s Baseline Detectors for 374 LSCOM Semantic Visual Concepts

emantic concept detection represents a key requirement in accessing large collections of digital mages/videos. Automatic detection of presence of a large number of semantic concepts, such as person,” or “waterfront,” or “explosion”, allows intuitive indexing and retrieval of visual content t the semantic level. Development of effective concept detectors and systematic evaluation ethods has become an active research topic in recent years. For example, a major video retrieval enchmarking event, NIST TRECVID[1], has contributed to this emerging area through (1) the rovision of large sets of common data and (2) the organization of common benchmark tasks to erform over this data.