Detail publikace

Brno University of Technology at TRECVid 2010 SIN, CCD

HRADIŠ, M.; BERAN, V.; ŘEZNÍČEK, I.; HEROUT, A.; BAŘINA, D.; VLČEK, A.; ZEMČÍK, P. Brno University of Technology at TRECVid 2010 SIN, CCD. In 2010 TREC Video Retrieval Evaluation Notebook Papers. Gaithersburg, MD: National Institute of Standards and Technology, 2010. p. 1-10.
Název česky
Brno University of Technology at TRECVid 2010
Typ
článek ve sborníku konference
Jazyk
anglicky
Autoři
Hradiš Michal, Ing., Ph.D. (UPGM)
Beran Vítězslav, doc. Ing., Ph.D. (UPGM)
Řezníček Ivo, Ing., Ph.D.
Herout Adam, prof. Ing., Ph.D. (UPGM)
Bařina David, Ing., Ph.D. (UPGM)
Vlček Adam, Ing.
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (UPGM)
URL
Klíčová slova

TRECVID, semantic indexing, Content-based Copy Detection, image classification

Abstrakt

This paper describes our approach to semantic indexing and content-based copy detection which was used for TRECVID 2010 evaluation.

Semantic indexing

1.  Theruns differ in the types of visual features used. All runs use severalbag-of-word representations fed to separate linear SVMs and the SVMs were fusedby logistic regression.

  • F_A_Brno_resource_4: Only single best visual features (on the trainingset) are used - dense image sampling with rgb-SIFT.
  • F_A_Brno_basic_3: This run uses dense sampling and Harris-Laplace detector incombination with SIFT and rgb-sift descriptors.
  • F_A_Brno_color_2: This run extends F_A_Brno_basic_3 by adding densesampling with rg-SIFT, Opponent-SIFT, Hue-SIFT, HSV-SIFT, C-SIFT and opponenthistogram descriptors.
  • F_A_Brno_spacetime_1: This run extends F_A_Brno_color_2 by adding space-timevisual features STIP and HESSTIP.

2. Combining multiple types of visualfeatures improves results significantly. F_A_Brno_color_2 achieve more thantwice better results than F_A_Brno_resource_4. The space-time visual featuresdid not improve results.

3. Combining multiple types of visualfeatures is important. Linear SVM is inferior to non-linear SVM in the contextof semantic indexing.

Content-based Copy Detection

1.    Two runs submitted, but with similar settings; the difference isonly in amount of processed test data (40% and 60%)

  • brno.m.*.l3sl2: SURF,bag-of-words (visual codebook: 2k size, 4 nearest neighbors used insoft-assignment), inverted file index, geometry (homography) based imagesimilarity metric

2.    What if any significant differences (in terms of what measures) didyou find among the runs?

  • only one setting used - nodifferences

3.    Based on the results, can you estimate the relative contribution ofeach component of your system/approach to its effectiveness?

  • slow search in referencedataset due to unsuitable configuration of used visual codebook

4.    Overall, what did you learn about runs/approaches and the researchquestion(s) that motivated them?

  • change the way of describingthe video content - frame based (or key-frame based) approach is not sufficient
Rok
2010
Strany
1–10
Sborník
2010 TREC Video Retrieval Evaluation Notebook Papers
Vydavatel
National Institute of Standards and Technology
Místo
Gaithersburg, MD
EID Scopus
2-s2.0-84905182947
BibTeX 
@inproceedings{BUT34908,
  author="Michal {Hradiš} and Vítězslav {Beran} and Ivo {Řezníček} and Adam {Herout} and David {Bařina} and Adam {Vlček} and Pavel {Zemčík}",
  title="Brno University of Technology at TRECVid 2010 SIN, CCD",
  booktitle="2010 TREC Video Retrieval Evaluation Notebook Papers",
  year="2010",
  pages="1--10",
  publisher="National Institute of Standards and Technology",
  address="Gaithersburg, MD",
  url="http://www-nlpir.nist.gov/projects/tvpubs/tv10.papers/brno.pdf"
}
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