Skip to main content

VERGE: A Multimodal Interactive Search Engine for Video Browsing and Retrieval

  • Conference paper
  • First Online:

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

Abstract

This paper presents VERGE interactive search engine, which is capable of browsing and searching into video content. The system integrates content-based analysis and retrieval modules such as video shot segmentation, concept detection, clustering, as well as visual similarity and object-based search.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    More information and demos of VERGE are available at: http://mklab.iti.gr/verge/

  2. 2.

    Latest VERGE system is available at: http://mklab-services.iti.gr/trec2015_v1/

References

  1. Schoeffmann, K.: A user-centric media retrieval competition: the video browser showdown 2012-2014. IEEE Multimedia 21(4), 8–13 (2014)

    Article  Google Scholar 

  2. Apostolidis, E., Mezaris, V.: Fast shot segmentation combining global and local visual descriptors. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6583–6587 (2014)

    Google Scholar 

  3. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)

    Google Scholar 

  4. Su, C.-W., Liao, H.-Y.M., Tyan, H.-R., Fan, K.-C., Chen, L.-H.: A motion-tolerant dissolve detection algorithm. IEEE Trans. Multimed. 7, 1106–1113 (2005)

    Article  Google Scholar 

  5. Seo, K.-D., Park, S., Jung, S.-H.: Wipe scene-change detector based on visual rhythm spectrum. IEEE Trans. Consum. Electron. 55(2), 831–838 (2009)

    Article  Google Scholar 

  6. Jegou, H., Douze, M., Schmid, C., Perez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of CVPR (2010)

    Google Scholar 

  7. Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 117–128 (2011)

    Article  Google Scholar 

  8. Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)

    Google Scholar 

  9. Markatopoulou, F., Mezaris, V., Pittaras, N., Patras, I.: Local features and a two-layer stacking architecture for semantic concept detection in video. IEEE Trans. Emerg. Topics Comput. 3(2), 193–204 (2015)

    Article  Google Scholar 

  10. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv technical report (2014)

    Google Scholar 

  11. Szegedy, C., et al.: Going deeper with convolutions. In: CVPR 2015 (2015). http://arxiv.org/abs/1409.4842

  12. Krizhevsky, A., Ilya, S., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing System, vol. 25, pp. 1097–1105. Curran Associates, Inc., (2012)

    Google Scholar 

  13. Markatopoulou, F., Mezaris, V., Patras, I.: Cascade of classifiers based on binary, non-binary and deep convolutional network descriptors for video concept detection. In: IEEE International Conference on Image Processing (ICIP 2015). IEEE, Canada (2015)

    Google Scholar 

  14. Safadi B., Quénot, G.: Re-ranking by local re-scoring for video indexing and retrieval. In: 20th ACM International Conference on Information and Knowledge Management, pp. 2081–2084 (2011)

    Google Scholar 

  15. Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014)

    Article  MathSciNet  Google Scholar 

  16. Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I.: Fast visual vocabulary construction for image retrieval using skewed-split k-d trees. In: Proceedings of the 22nd International Conference on MultiMedia Modeling (MMM 2016), Miami (2016)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the European Commission under contracts FP7-600826 ForgetIT, FP7-610411 MULTISENSOR and FP7-312388 HOMER.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anastasia Moumtzidou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Moumtzidou, A. et al. (2016). VERGE: A Multimodal Interactive Search Engine for Video Browsing and Retrieval. 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_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27674-8_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics