Article

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

Authors:

Cees G. M. Snoek,

Marcel Worring,

Jan C. van Gemert,

Jan-Mark Geusebroek,

Arnold W. M. SmeuldersAuthors Info & Claims

MM '06: Proceedings of the 14th ACM international conference on Multimedia

Pages 421 - 430

https://doi.org/10.1145/1180639.1180727

Published: 23 October 2006 Publication History

Abstract

We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect 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 offering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that inuence video indexing performance, the challenge offers to the research community a manually annotated concept lexicon, pre-computed low-level multimedia features, trained classifier models, and five experiments together with baseline performance, which are all available at http://www.mediamill.nl/challenge/.

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Index Terms

The challenge problem for automated detection of 101 semantic concepts in multimedia
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Information & Contributors

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Published In

cover image ACM Conferences

MM '06: Proceedings of the 14th ACM international conference on Multimedia

October 2006

1072 pages

ISBN:1595934472

DOI:10.1145/1180639

General Chairs:
Klara Nahrstedt
UIUC
,
Matthew Turk
UCSB
,
Program Chairs:
Yong Rui
Microsoft Research
,
Wolfgang Klas
Universität Wien
,
Ketan Mayer-Patel
UNC

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2006

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MM06: The 14th ACM International Conference on Multimedia 2006

October 23 - 27, 2006

CA, Santa Barbara, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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https://dl.acm.org/doi/10.1145/3664647.3681300
Nicola VDelgado KLauretto M(2024)Imbalance-Robust Multi-Label Self-Adjusting kNNACM Transactions on Knowledge Discovery from Data10.1145/366357518:8(1-30)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3663575
Guo WWang AThymes BJoachims TBaeza-Yates RBonchi F(2024)Ranking with Slot ConstraintsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672000(956-967)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672000
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