Adaptive Multimedia Retrieval: User, Context, and Feedback: by Myunggwon Hwang, Hyunjang Kong, Sunkyoung Baek, Pankoo Kim

By Myunggwon Hwang, Hyunjang Kong, Sunkyoung Baek, Pankoo Kim (auth.), Stéphane Marchand-Maillet, Eric Bruno, Andreas Nürnberger, Marcin Detyniecki (eds.)

This booklet constitutes the completely refereed post-proceedings of the 4th overseas Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006. The 18 revised complete papers provided including 2 invited papers have been conscientiously chosen in the course of rounds of reviewing and development. additionally incorporated are invited contributions which have been meant to open on less-addressed themes in the neighborhood, because it is the case for song info retrieval and allotted info retrieval. The papers are geared up in topical sections on ontology-based retrieval and annotation, rating and similarity measurements, track details retrieval, visible modelling, adaptive retrieval, structuring multimedia, in addition to consumer integration and profiling.

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Additional info for Adaptive Multimedia Retrieval: User, Context, and Feedback: 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers

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For the starting point, key frames of videos must be segmented into regions. ) are extracted for each region. If some regions have similar properties for a period of time (consecutive keyframes), it seems to be a possible object [13]. By using similarity functions, objects identified from regions are assigned to their actual Ontology-Supported Video Modeling and Retrieval 33 names by using information gained from the training set/experts. For each object, a vector of descriptor values is used to represent a related object.

8. P. Ciaccia, D. Montesi, W. Penzo, and A. Trombetta. Imprecision and user preferences in multimedia queries: A generic algebraic approach. In Proc. of FoIKS: Foundations of Information and Knowledge Systems, pages 50–71. Springer, 2000. 9. R. Fagin. Fuzzy Queries in Multimedia Database Systems. In Proc. of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1-3, 1998, Seattle, Washington, pages 1–10. ACM Press, 1998. 10. H. -S. Chua. A bootstrapping approach to annotating large image collection.

Either by experts or by using a training set, the properties of objects and relations between objects are determined. Object classification is done by: – Low-level feature values like color distribution, shape, texture. – Spatial knowledge. – Spatio-temporal change. (A set of consecutive key frames must be searched to gain knowledge for this property) Extraction phase starts when we have enough knowledge to separate one object from others. For each video, object extraction is done at first. For each separated region, descriptor vectors are calculated.

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