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This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives
Indexing, Video sharing, Production, Detectors, Information analysis, Pattern analysis, NIST, Multimedia systems, Information systems, Pattern recognition,pattern recognition., Video analysis, concept learning, benchmarking, content analysis and indexing, multimedia information systems
"The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1678-1689, October 2006, doi:10.1109/TPAMI.2006.212
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