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International Conference on Multimedia Modeling

MMM 2022: MultiMedia Modeling pp 543–548Cite as

VISIONE at Video Browser Showdown 2022

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13142)

Abstract

VISIONE is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In the latest version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.

Keywords

  • Content-based video retrieval
  • Video search
  • Information search and retrieval
  • Surrogate text representation

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References

  1. Amato, G., et al.: VISIONE at VBS2019. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W.-H., Vrochidis, S. (eds.) MMM 2019. LNCS, vol. 11296, pp. 591–596. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05716-9_51

    CrossRef  Google Scholar 

  2. Amato, G., et al.: The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. J. Imaging 7(5), 76 (2021)

    CrossRef  Google Scholar 

  3. Amato, G., et al.: VISIONE at video browser showdown 2021. In: Lokoč, J., et al. (eds.) MMM 2021. LNCS, vol. 12573, pp. 473–478. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67835-7_47

    CrossRef  Google Scholar 

  4. Benavente, R., Vanrell, M., Baldrich, R.: Parametric fuzzy sets for automatic color naming. JOSA A 25(10), 2582–2593 (2008)

    CrossRef  Google Scholar 

  5. Berlin, B., Kay, P.: Basic Color Terms: Their Universality and Evolution. University of California Press, Berkeley (1991)

    Google Scholar 

  6. Berns, F., Rossetto, L., Schoeffmann, K., Beecks, C., Awad, G.: V3C1 dataset: an evaluation of content characteristics. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval, pp. 334–338. Association for Computing Machinery (2019)

    Google Scholar 

  7. Boynton, R.M., Olson, C.X.: Salience of chromatic basic color terms confirmed by three measures. Vision. Res. 30(9), 1311–1317 (1990)

    CrossRef  Google Scholar 

  8. Gordo, A., Almazan, J., Revaud, J., Larlus, D.: End-to-end learning of deep visual representations for image retrieval. Int. J. Comput. Vision 124(2), 237–254 (2017)

    CrossRef  MathSciNet  Google Scholar 

  9. Heller, S., et al.: Towards explainable interactive multi-modal video retrieval with vitrivr. In: Lokoč, J., et al. (eds.) MMM 2021. LNCS, vol. 12573, pp. 435–440. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67835-7_41

    CrossRef  Google Scholar 

  10. Lokoč, J., et al.: Is the reign of interactive search eternal? Findings from the video browser showdown 2020. ACM Trans. Multimed. Comput. Commun. Appl. 17(3), 1–26 (2021)

    CrossRef  Google Scholar 

  11. Messina, N., Amato, G., Esuli, A., Falchi, F., Gennaro, C., Marchand-Maillet, S.: Fine-grained visual textual alignment for cross-modal retrieval using transformer encoders. arXiv preprint arXiv:2008.05231 (2020)

  12. Messina, N., Falchi, F., Esuli, A., Amato, G.: Transformer reasoning network for image-text matching and retrieval. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5222–5229. IEEE (2021)

    Google Scholar 

  13. Peška, L., Kovalčík, G., Souček, T., Škrhák, V., Lokoč, J.: W2VV++ BERT model at VBS 2021. In: Lokoč, J., et al. (eds.) MMM 2021. LNCS, vol. 12573, pp. 467–472. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67835-7_46

    CrossRef  Google Scholar 

  14. Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 (2021)

  15. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. CoRR abs/1804.02767 (2018)

    Google Scholar 

  16. Revaud, J., Almazan, J., Rezende, R., de Souza, C.: Learning with average precision: training image retrieval with a listwise loss. In: International Conference on Computer Vision, pp. 5106–5115. IEEE (2019)

    Google Scholar 

  17. Rossetto, L., et al.: Interactive video retrieval in the age of deep learning - detailed evaluation of VBS 2019. IEEE Trans. Multimedia 23, 243–256 (2020)

    CrossRef  Google Scholar 

  18. Rossetto, L., Schoeffmann, K., Bernstein, A.: Insights on the V3C2 dataset. arXiv preprint arXiv:2105.01475 (2021)

  19. Sturges, J., Whitfield, T.A.: Salient features of munsell colour space as a function of monolexemic naming and response latencies. Vision. Res. 37(3), 307–313 (1997)

    CrossRef  Google Scholar 

  20. Van De Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)

    CrossRef  MathSciNet  Google Scholar 

  21. Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: VarifocalNet: an IoU-aware dense object detector. In: Conference on Computer Vision and Pattern Recognition, pp. 8514–8523. IEEE, June 2021

    Google Scholar 

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Acknowledgements

This work was partially funded by AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911); AI4EU project (EC, H2020, n. 825619); AI4ChSites, CNR4C program (Tuscany POR FSE 2014-2020 CUP B15J19001040004).

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Authors and Affiliations

  1. ISTI-CNR, Via G. Moruzzi 1, 56124, Pisa, Italy

    Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Messina, Lucia Vadicamo & Claudio Vairo

Authors
  1. Giuseppe Amato
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  2. Paolo Bolettieri
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  3. Fabio Carrara
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  4. Fabrizio Falchi
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  5. Claudio Gennaro
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  6. Nicola Messina
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  7. Lucia Vadicamo
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  8. Claudio Vairo
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Corresponding author

Correspondence to Nicola Messina .

Editor information

Editors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Björn Þór Jónsson

  2. Dublin City University, Dublin, Ireland

    Dr. Cathal Gurrin

  3. University of Science, VNU-HCM, Ho Chi Minh City, Vietnam

    Minh-Triet Tran

  4. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  5. National Tsing Hua University, Hsinchu, Taiwan

    Anita Min-Chun Hu

  6. Hanoi University of Science and Technology, Hanoi, Vietnam

    Binh Huynh Thi Thanh

  7. Median Technologies, Valbonne, France

    Dr. Benoit Huet

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Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_52

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_52

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