VERGE in VBS 2017

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)


This paper presents VERGE interactive video retrieval engine, which is capable of browsing and searching into video content. The system integrates several content-based analysis and retrieval modules including concept detection, clustering, visual similarity search, object-based search, query analysis and multimodal and temporal fusion.


Convolutional Neural Network Video Retrieval Video Shot Original Query Temporal Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the EU’s Horizon 2020 research and innovation programme under grant agreements H2020-687786 InVID, H2020-693092 MOVING, H2020-645012 KRISTINA and H2020-700024 TENSOR.


  1. 1.
    Cobârzan, C., et al.: Interactive video search tools: a detailed analysis of the video browser showdown 2015. In: Multimedia Tools and Applications, pp. 1–33 (2015)Google Scholar
  2. 2.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  3. 3.
    Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 117–128 (2011)CrossRefGoogle Scholar
  4. 4.
    Markatopoulou, F., et al.: ITI-CERTH participation to TRECVID 2015. In: TRECVID 2015 Workshop, Gaithersburg, MD, USA (2015)Google Scholar
  5. 5.
    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
  6. 6.
    Tzelepis, C., Galanopoulos, D., Mezaris, V., and Patras, I.: Learning to detect video events from zero or very few video examples. In: Image and Vision Computing (2015)Google Scholar
  7. 7.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJCAI 7, 1606–1611 (2007)Google Scholar
  8. 8.
    Barthel, K. U., Hezel, N., and Mackowiak, R.: ImageMap - Visually Browsing Millions of Images. In: MultiMedia Modeling, pp. 287–290 (2015)Google Scholar
  9. 9.
    Moumtzidou, A., et al.: A multimedia interactive search engine based on graph-based and non-linear multimodal fusion. In: CBMI 2016 International Workshop, pp. 1–4 (2016)Google Scholar
  10. 10.
    t-SNE visualization of CNN codes.
  11. 11.
    Gialampoukidis, I., et al.: A hybrid graph-based and non-linear late fusion approach for multimedia retrieval. In: CBMI 2016 International Workshop, pp. 1–6. IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Information Technologies Institute/Centre for Research and Technology HellasThermi-ThessalonikiGreece
  2. 2.School of Electronic Engineering and Computer ScienceQMULLondonUK

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