Multimedia Tools and Applications

, Volume 75, Issue 15, pp 8973–8997 | Cite as

A comparative study for multiple visual concepts detection in images and videos

  • Abdelkader Hamadi
  • Philippe Mulhem
  • Georges Quénot
Article
  • 136 Downloads

Abstract

Automatic indexing of images and videos is a highly relevant and important research area in multimedia information retrieval. The difficulty of this task is no longer something to prove. Most efforts of the research community have been focusing, in the past, on the detection of single concepts in images/videos, which is already a hard task. With the evolution of information retrieval systems, users’ needs become more abstract, and lead to a larger number of words composing the queries. It is important to think about indexing multimedia documents with more than just individual concepts, to help retrieval systems to answer such complex queries. Few studies addressed specifically the problem of detecting multiple concepts (multi-concept) in images and videos. Most of them concern the detection of concept pairs. These studies showed that such challenge is even greater than the one of single concept detection. In this work, we address the problem of multi-concept detection in images/videos by making a comparative and detailed study. Three types of approaches are considered: 1) building detectors for multi-concept, 2) fusing single concepts detectors and 3) exploiting detectors of a set of single concepts in a stacking scheme. We conducted our evaluations on PASCAL VOC’12 collection regarding the detection of pairs and triplets of concepts. We extended the evaluation process on TRECVid 2013 dataset for infrequent concept pairs’ detection. Our results show that the three types of approaches give globally comparable results for images, but they differ for specific kinds of pairs/triplets. In the case of videos, late fusion of detectors seems to be more effective and efficient when single concept detectors have good performances. Otherwise, directly building bi-concept detectors remains the best alternative, especially if a well-annotated dataset is available. The third approach did not bring additional gain or efficiency.

Keywords

Semantic indexing Multimedia Fusion Multiple concepts Multi-concept Concept pairs Triplet of concepts Bi-concept Tri-concept Image Video Pascal VOC TRECVid 

Notes

Acknowledgments

This work was partly realized as part of the Quaero Program funded by OSEO, French State agency for innovation. This work was supported in part by the French project VideoSense ANR-09-CORD-026 of the ANR. Experiments presented in this paper were carried out using the Grid’5000 experimental test bed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr). The authors wish to thanks the participants of the IRIM (Indexation et Recherche d’Information Multimédia) group of the GDR-ISIS research network from CNRS for providing the descriptors used in these experiments.

References

  1. 1.
    Aly R, Hiemstra D, de Vries A, de Jong F (2008) A probabilistic ranking framework using unobservable binary events for video search. In: 7th ACM international conference on content-based image and video retrieval, CIVR 2008, pp 349–358. ACM, New York, NY, USAGoogle Scholar
  2. 2.
    Ayache S, Quénot G (2008) Video corpus annotation using active learning. In: Proceedings of the IR research, ECIR’08, pp 187–198. Springer-Verlag, Berlin, HeidelbergGoogle Scholar
  3. 3.
    Ballas N, Labbé B, Shabou A, Le Borgne H, Gosselin P, Redi M, Merialdo B, Jégou H, Delhumeau J, Vieux R, Mansencal B, Benois-Pineau J, Ayache S, Hamadi A, Safadi B, Thollard F, Derbas N, Quénot G, Bredin H, Cord M, Gao B, Zhu C, tang Y, Dellandrea E, Bichot CE, Chen L, Benot A, Lambert P, Strat T, Razik J, Paris S, Glotin H, Ngoc Trung T, Petrovska Delacrétaz D, Chollet G, Stoian A, Crucianu M (2012) IRIM at TRECVID 2012: semantic indexing and instance search. In: Proc. TRECVID Workshop. Gaithersburg, MD, USAGoogle Scholar
  4. 4.
    Brown L, Cao L, Chang SF, Cheng Y, Choudhary A, Codella N, Cotton C, Ellis D, Fan Q, Feris R, Gong L, Hill M, Hua G, Kender J, Merler M, Mu Y, Pankanti S, Smith JR, Yu FX (2013) Ibm research and columbia university trecvid-2013 multimedia event detection (med), multimedia event recounting (mer), surveillance event detection (sed), and semantic indexing (sin) systems. In: Proc. TRECVID Workshop. Gaithersburg, MD, USAGoogle Scholar
  5. 5.
    Chang SF, Hsu W, Jiang W, Kennedy L, Xu D, Yanagawa A, Zavesky E (2006) Columbia university trecvid-2006 video search and high-level feature extraction. in proc. trecvid workshop. In: Proc. TRECVID WorkshopGoogle Scholar
  6. 6.
    Chen SC, Shyu ML, Chen M (2008) An effective multi-concept classifier for video streams. In: 2008 IEEE international conference on semantic computing, pp 80–87, doi:10.1109/ICSC.2008.72, (to appear in print)
  7. 7.
    Hamadi A, Mulhem P, Quenot G (2013) Conceptual feedback for semantic multimedia indexing. In: 2013 11th international workshop on content-based multimedia indexing (CBMI), pp 53–58, doi:10.1109/CBMI.2013.6576552, (to appear in print)
  8. 8.
    Hamadi A, Mulhem P, Qunot G (2014) Extended conceptual feedback for semantic multimedia indexing. Multimedia Tools and Applications pp 1–24Google Scholar
  9. 9.
    Hamadi A, Safadi B, Vuong TTT, Han D, Derbas N, Mulhem P, Qunot G. (2013) Quaero at TRECVID 2013: Semantic Indexing and Instance Search. In: Proc. TRECVID Workshop. Gaithersburg, MD, USAGoogle Scholar
  10. 10.
    Ishikawa S, Koskela M, Sjoberg M, Laaksonen J, Oja E, Amid E, Palomaki K, Mesaros A, Kurimo M (2013) Picsom experiments in trecvid 2013. In: Proc. TRECVID Workshop. Gaithersburg, MD, USAGoogle Scholar
  11. 11.
    Jiang W (2010) Advanced techniques for semantic concept detection in general videos. Ph.D. thesis, Columbia UniversityGoogle Scholar
  12. 12.
    Li X, Snoek CGM, Worring M, Smeulders A (2012) Harvesting social images for bi-concept search. IEEE Trans Multimedia 14(4):1091–1104CrossRefGoogle Scholar
  13. 13.
    Li X, Wang D, Li J, Zhang B (2007) Video search in concept subspace: A text-like paradigm. In: Proc. of CIVRGoogle Scholar
  14. 14.
    Platt J (2000) Probabilistic outputs for support vector machines and comparison to regularize likelihood methods. In: Advances in Large Margin Classifiers, pp 61–74Google Scholar
  15. 15.
    Qi GJ, Hua XS, Rui Y, Tang J, Mei T, Zhang HJ, Prasad AR (2007) Correlative multi-label video annotation. In: Lienhart R, Hanjalic A, Choi S, Bailey BP, Sebe N (eds) Proceedings of the 15th international conference on multimedia 2007, Augsburg, Germany, September 24-29, 2007. ACM, pp 17–26. doi:10.1145/1291233.1291245
  16. 16.
    Safadi B, Quénot G (2010) Evaluations of multi-learner approaches for concept indexing in video documents. In: RIAO, pp 88–91Google Scholar
  17. 17.
    Safadi B, Qunot G (2013) Descriptor Optimization for Multimedia Indexing and Retrieval. In: CBMI 2013, 11th international workshop on content-based multimedia indexing. Veszprem, HUNGARYGoogle Scholar
  18. 18.
    Salton G, Fox EA, Wu H (1983) Extended boolean information retrieval. Commun ACM 26(11):1022–1036. doi:10.1145/182.358466 MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Salton G, Fox EA, Wu H (1983) Extended boolean information retrieval. Commun ACM 26(11):1022–1036MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Smith JR, Naphade M, Natsev A (2003) Multimedia semantic indexing using model vectors. In: Proceedings of ICME - Volume 1, pp 445–448. IEEE Computer Society, Washington, DC, USA. http://dl.acm.org/citation.cfm?id=1153922.1154410
  21. 21.
    Snoek CG, Huurnink B, Hollink L, de Rijke M, Schreiber G, Worring M (2007) Adding semantics to detectors for video retrieval. Trans Multi 9(5):975–986CrossRefGoogle Scholar
  22. 22.
    Wang G, Forsyth DA (2009) Joint learning of visual attributes, object classes and visual saliency. In: ICCV 09, pp 537–544Google Scholar
  23. 23.
    Wei XY, Jiang YG, Ngo CW (2011) Concept-driven multi-modality fusion for video search. IEEE Trans Circuits Syst Video Technol 21(1):62–73CrossRefGoogle Scholar
  24. 24.
    Weng MF, Chuang YY Multi-cue fusion for semantic video indexing. In: Proceeding of the 16th ACM international conference on multimedia, MM 08, pp 71-80, New York, NY, USA, 2008. ACM. ACM ID : 1459370Google Scholar
  25. 25.
    Wolpert DH (1992) Stacked generalization. Neural Netw 5:241–259CrossRefGoogle Scholar
  26. 26.
    Xie L, Yan R, Yang J (2008) Multi-concept learning with large-scale multimedia lexicons. In: 15th IEEE international conference on image processing, ICIP 2008, pp 2148–2151, doi:10.1109/ICIP.2008.4712213, (to appear in print)
  27. 27.
    Yan R, Hauptmann AG (2003) The combination limit in multimedia retrieval. In: Proceedings of the eleventh ACM international conference on multimedia, pp 339–342Google Scholar
  28. 28.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Abdelkader Hamadi
    • 1
  • Philippe Mulhem
    • 2
  • Georges Quénot
    • 2
  1. 1.Université de LorraineNancy CedexFrance
  2. 2.Univ. Grenoble Alpes, CNRS, LIGGrenobleFrance

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