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iAutoMotion – an Autonomous Content-Based Video Retrieval Engine

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9517))

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Abstract

This paper introduces iAutoMotion, an autonomous video retrieval system that requires only minimal user input. It is based on the video retrieval engine IMOTION. iAutoMotion uses a camera to capture the input for both visual and textual queries and performs query composition, retrieval, and result submission autonomously. For the visual tasks, it uses various visual features applied to the captured query images; for the textual tasks, it applies OCR and some basic natural language processing, combined with object recognition. As the iAutoMotion system does not conform to the VBS 2016 rules, it will participate as unofficial competitor and serve as a benchmark for the manually operated systems.

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Notes

  1. 1.

    https://rednoise.org/rita/.

References

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Acknowlegements

This work was partly supported by the Chist-Era project IMOTION with contributions from the Belgian Fonds de la Recherche Scientifique (FNRS, contract no. R.50.02.14.F), the Scientific and Technological Research Council of Turkey (Tübitak, grant no. 113E325), and the Swiss National Science Foundation (SNSF, contract no. 20CH21_151571).

Author information

Authors and Affiliations

  1. Databases and Information Systems Research Group, Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland

    Luca Rossetto, Ivan Giangreco, Claudiu Tănase & Heiko Schuldt

  2. Research Center in Information Technologies, Université de Mons, Mons, Belgium

    Stéphane Dupont & Omar Seddati

  3. Intelligent User Interfaces Lab, Koç University, Istanbul, Turkey

    Metin Sezgin & Yusuf Sahillioğlu

Authors
  1. Luca Rossetto
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  2. Ivan Giangreco
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  3. Claudiu Tănase
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  4. Heiko Schuldt
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  5. Stéphane Dupont
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  6. Omar Seddati
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  7. Metin Sezgin
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  8. Yusuf Sahillioğlu
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Corresponding author

Correspondence to Luca Rossetto .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Rossetto, L. et al. (2016). iAutoMotion – an Autonomous Content-Based Video Retrieval Engine. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_37

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  • DOI: https://doi.org/10.1007/978-3-319-27674-8_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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