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The challenge problem for automated detection of 101 semantic concepts in multimedia

The challenge problem for automated detection of 101 semantic concepts in multimedia The Challenge Problem for Automated Detection of 101 Semantic Concepts in Multimedia Cees G.M. Snoek, Marcel Worring, Jan C. van Gemert, Jan-Mark Geusebroek, and Arnold W.M. Smeulders {cgmsnoek, ISLA, Informatics Institute, University of Amsterdam Kruislaan 403, 1098 SJ, Amsterdam, The Netherlands worring, jvgemert, mark, smeulders}@science.uva.nl 1. INTRODUCTION ABSTRACT We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that a €ect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously o €ering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that in ‚uence video indexing performance, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

The challenge problem for automated detection of 101 semantic concepts in multimedia

Association for Computing Machinery — Oct 23, 2006

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References (34)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2006 by ACM Inc.
ISBN
1-59593-447-2
doi
10.1145/1180639.1180727
Publisher site
See Article on Publisher Site

Abstract

The Challenge Problem for Automated Detection of 101 Semantic Concepts in Multimedia Cees G.M. Snoek, Marcel Worring, Jan C. van Gemert, Jan-Mark Geusebroek, and Arnold W.M. Smeulders {cgmsnoek, ISLA, Informatics Institute, University of Amsterdam Kruislaan 403, 1098 SJ, Amsterdam, The Netherlands worring, jvgemert, mark, smeulders}@science.uva.nl 1. INTRODUCTION ABSTRACT We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that a €ect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously o €ering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that in ‚uence video indexing performance,

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