The Mediamill Semantic Video Search Engine

In this paper we present the methods underlying the MediaMill semantic video search engine. The basis for the engine is a semantic indexing process which is currently based on a lexicon of 491 concept detectors. To support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread space, and browsers to explore them. We compare the different browsers and their utility within the TRECVID benchmark. In 2005, we obtained a top-3 result for 19 out of 24 search topics. In 2006 for 14 out of 24.

[1]  John Adcock,et al.  Interactive Video Search Using Multilevel Indexing , 2005, CIVR.

[2]  Cor J. Veenman,et al.  Robust Scene Categorization by Learning Image Statistics in Context , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[3]  Dennis Koelma,et al.  The MediaMill TRECVID 2008 Semantic Video Search Engine , 2008, TRECVID.

[4]  Alan F. Smeaton,et al.  Large Scale Evaluations of Multimedia Information Retrieval: The TRECVid Experience , 2005, CIVR.

[5]  Timo Ojala,et al.  Cluster-temporal browsing of large news video databases , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[6]  Christian Petersohn.  Fraunhofer HHI at TRECVID 2004: Shot Boundary Detection System , 2004, TRECVID.

[7]  Marcel Worring,et al.  MediaMill: exploring news video archives based on learned semantics , 2005, MULTIMEDIA '05.

[8]  Marcel Worring,et al.  Interactive access to large image collections using similarity-based visualization , 2008, J. Vis. Lang. Comput..

[9]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[10]  Marcel Worring,et al.  The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Stefan M. Rüger,et al.  Three Interfaces for Content-Based Access to Image Collections , 2004, CIVR.

[12]  Alexander G. Hauptmann,et al.  The Use and Utility of High-Level Semantic Features in Video Retrieval , 2005, CIVR.

[13]  Rong Yan,et al.  Extreme video retrieval: joint maximization of human and computer performance , 2006, MM '06.