By Myunggwon Hwang, Hyunjang Kong, Sunkyoung Baek, Pankoo Kim (auth.), Stéphane Marchand-Maillet, Eric Bruno, Andreas Nürnberger, Marcin Detyniecki (eds.)
This publication constitutes the completely refereed post-proceedings of the 4th foreign 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 issues in the neighborhood, because it is the case for track info retrieval and allotted info retrieval. The papers are equipped in topical sections on ontology-based retrieval and annotation, rating and similarity measurements, tune details retrieval, visible modelling, adaptive retrieval, structuring multimedia, in addition to person 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
3 Text and Visual Features This section describes the features used to represent text queries and pictures. e. each query q is represented with a vocabulary sized vector, q = (q1 , . . , qT ), where qi is the weight of term i in q and T is the vocabulary size. e. qi = tfi,q · idfi T j=1 (tfj,q · idfj )2 where tfi,q refers to the number of occurrences of i in q and idfi = −log(ri ), ri being the fraction of training captions in which i occurs. g. [1,6], we adopt a bag-of-visterms representation for pictures.
Tr Abstract. Current solutions are still far from reaching the ultimate goal, namely to enable users to retrieve the desired video clip among massive amounts of visual data in a semantically meaningful manner. With this study we propose a video database model that provides nearly automatic object, event and concept extraction. It provides a reasonable approach to bridging the gap between low-level representative features and high-level semantic contents from a human point of view. By using training sets and expert opinions, low-level feature values for objects and relations between objects are determined.
I. Schmitt. Basic Concepts for Unifying Queries of Database and Retrieval Systems. Technical Report 7, Fakult¨ at f¨ ur Informatik, Univ. Magdeburg, 2005. 27. I. Schmitt and N. Schulz. Similarity Relational Calculus and its Reduction to a Similarity Algebra. In Proc. of 3rd Intern. Symposium on Foundations of Information and Knowledge Systems (FoIKS’04), pages 252–272. Springer-Verlag, 2004. 28. I. Schmitt, N. Schulz, and T. Herstel. WS-QBE: A QBE-like Query Language for Complex Multimedia Queries.
Adaptive Multimedia Retrieval: User, Context, and Feedback: 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers by Myunggwon Hwang, Hyunjang Kong, Sunkyoung Baek, Pankoo Kim (auth.), Stéphane Marchand-Maillet, Eric Bruno, Andreas Nürnberger, Marcin Detyniecki (eds.)