[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Explainable event recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The literature shows outstanding capabilities for Convolutional Neural Networks (CNNs) in event recognition in images. However, fewer attempts are made to analyze the potential causes behind the decisions of the models and explore whether the predictions are based on event-salient objects/regions? To explore this important aspect of event recognition, in this work, we propose an explainable event recognition framework relying on Grad-CAM and an Xception architecture-based CNN model. Experiments are conducted on four large-scale datasets covering a diversified set of natural disasters, social, and sports events. Overall, the model showed outstanding generalization capabilities obtaining overall F1 scores of 0.91, 0.94, and 0.97 on natural disasters, social, and sports events, respectively. Moreover, for subjective analysis of activation maps generated through Grad-CAM for the predicted samples of the model, a crowd-sourcing study is conducted to analyze whether the model’s predictions are based on event-related objects/regions or not? The results of the study indicate that 78%, 84%, and 78% of the model decisions on natural disasters, sports, and social events datasets, respectively, are based on event-related objects/regions.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Adadi A, Berrada M (2020) Explainable ai for healthcare: from black box to interpretable models. In: Embedded systems and artificial intelligence, pp 327–337. Springer

  2. Afridi YS, Ahmad K, Hassan L (2021) Artificial intelligence based prognostic maintenance of renewable energy systems: a review of techniques, challenges, and future research directions. International Journal of Energy Research

  3. Ahmad K, Conci N (2019) How deep features have improved event recognition in multimedia: a survey. ACM Trans Multimed Comput Commun Applic (TOMM) 15(2):1–27

    Article  Google Scholar 

  4. Ahmad K, Conci N, Boato G, De Natale F (2016) Used: a large-scale social event detection dataset. In: Proceedings of the 7th international conference on multimedia systems, pp 1–6

  5. Ahmad K, Conci N, De Natale F (2018) A saliency-based approach to event recognition. Signal Process Image Commun 60:42–51

    Article  Google Scholar 

  6. Ahmad K, Maabreh M, Ghaly M, Khan K, Qadir J, Al-Fuqaha A (2022) Developing future human-centered smart cities: critical analysis of smart city security, data management, and ethical challenges. Comput Sci Rev 43 (100):452

    Google Scholar 

  7. Ahmad K, Mekhalfi ML, Conci N, Boato G, Melgani F, De Natale F (2017) A pool of deep models for event recognition. In: 2017 IEEE international conference on image processing (ICIP), pp 2886–2890. IEEE

  8. Ahmad K, Mekhalfi ML, Conci N, Melgani F, Natale FD (2018) Ensemble of deep models for event recognition. ACM Trans Multimed Comput Commun Applic (TOMM) 14(2):1–20

    Article  Google Scholar 

  9. Ahmad K, Pogorelov K, Riegler M, Conci N, Halvorsen P (2019) Social media and satellites: disaster event detection, linking and summarization. Multimed Tools Appl 78(3):2837–2875

    Article  Google Scholar 

  10. Ahmad K, Sohail A, Conci N, De Natale F (2018) A comparative study of global and deep features for the analysis of user-generated natural disaster related images. In: 2018 IEEE 13th image, video, and multidimensional signal processing workshop (IVMSP), pp 1–5. IEEE

  11. Ahsan U, Sun C, Hays J, Essa I (2017) Complex event recognition from images with few training examples. In: 2017 IEEE winter conference on applications of computer vision (WACV), pp 669–678. IEEE

  12. Baro X, Gonzalez J, Fabian J, Bautista MA, Oliu M, Jair Escalante H, Guyon I, Escalera S (2015) Chalearn looking at people 2015 challenges: action spotting and cultural event recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1–9

  13. Chandrakala S, Venkatraman M, Shreyas N, Jayalakshmi S (2021) Multi-view representation for sound event recognition. SIViP, 1–9

  14. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

  15. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE Conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 248–255. IEEE

  16. Fiok K, Farahani FV, Karwowski W, Ahram T (2021) Explainable artificial intelligence for education and training. The Journal of Defense Modeling and Simulation, 15485129211028651

  17. Francois AR, Nevatia R, Hobbs J, Bolles RC, Smith JR (2005) Verl: an ontology framework for representing and annotating video events. IEEE Multimed 12(4):76–86

    Article  Google Scholar 

  18. Gade K, Geyik SC, Kenthapadi K, Mithal V, Taly A (2019) Explainable ai in industry. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3203–3204

  19. Gan C, Wang N, Yang Y, Yeung DY, Hauptmann AG (2015) Devnet: a deep event network for multimedia event detection and evidence recounting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2568–2577

  20. Li LJ, Fei-Fei L (2007) What, where and who? Classifying events by scene and object recognition. In: 2007 IEEE 11th international conference on computer vision, pp 1–8. IEEE

  21. Liu M, Liu X, Li Y, Chen X, Hauptmann AG, Shan S (2015) Exploiting feature hierarchies with convolutional neural networks for cultural event recognition. In: Proceedings of the IEEE international conference on computer vision workshops, pp 32–37

  22. Mattivi R, Uijlings J, De Natale F, Sebe N (2011) Exploitation of time constraints for (sub-) event recognition. In: Proceedings of the 2011 joint ACM workshop on modeling and representing events, pp 7–12

  23. Papadopoulos S, Troncy R, Mezaris V, Huet B, Kompatsiaris I (2011) Social event detection at mediaeval 2011: challenges, dataset and evaluation. In: MediaEval

  24. Park S, Kwak N (2015) Cultural event recognition by subregion classification with convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 45–50

  25. Rosani A, Boato G, De Natale F (2015) Eventmask: a game-based framework for event-saliency identification in images. IEEE Trans Multimed 17 (8):1359–1371

    Article  Google Scholar 

  26. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  27. Said N, Ahmad K, Riegler M, Pogorelov K, Hassan L, Ahmad N, Conci N (2019) Natural disasters detection in social media and satellite imagery: a survey. Multimed Tools Applic 78(22):31,267–31,302

    Article  Google Scholar 

  28. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

  29. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  30. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A et al (2015) Going deeper with convolutions. Cvpr

  31. Wang L, Wang Z, Du W, Qiao Y (2015) Object-scene convolutional neural networks for event recognition in images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 30–35

  32. Wang L, Wang Z, Qiao Y, Van Gool L (2018) Transferring deep object and scene representations for event recognition in still images. Int J Comput Vis 126(2):390–409

    Article  MathSciNet  Google Scholar 

  33. Wei X, Gao BB, Wu J (2015) Deep spatial pyramid ensemble for cultural event recognition. In: Proceedings of the IEEE international conference on computer vision workshops, pp 38–44

  34. Xiong Y, Zhu K, Lin D, Tang X (2015) Recognize complex events from static images by fusing deep channels. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE

  35. Yang S, Gao T, Wang J, Deng B, Lansdell B, Linares-Barranco B (2021) Efficient spike-driven learning with dendritic event-based processing. Front Neurosci 15:97

    Article  Google Scholar 

  36. Yang S, Wang J, Hao X, Li H, Wei X, Deng B, Loparo KA (2021) Bicoss: toward large-scale cognition brain with multigranular neuromorphic architecture. IEEE Transactions on Neural Networks and Learning Systems

  37. Yang S, Wang J, Zhang N, Deng B, Pang Y, Azghadi MR (2021) Cerebellumorphic: large-scale neuromorphic model and architecture for supervised motor learning. IEEE Transactions on Neural Networks and Learning Systems

  38. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929

  39. Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Advances in neural information processing systems, pp 487–495

Download references

Author information

Authors and Affiliations

  1. Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Imran Khan

  2. Department of Computer Science, Munster Technological University, Cork, Ireland

    Kashif Ahmad

  3. School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, Islamabad, Pakistan

    Namra Gul

  4. Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Talhat Khan & Nasir Ahmad

  5. Hamad Bin Khalifa University, Doha, Qatar

    Ala Al-Fuqaha

Authors
  1. Imran Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kashif Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Namra Gul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Talhat Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nasir Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ala Al-Fuqaha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kashif Ahmad.

Ethics declarations

Conflict of Interests

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, I., Ahmad, K., Gul, N. et al. Explainable event recognition. Multimed Tools Appl 82, 40531–40557 (2023). https://doi.org/10.1007/s11042-023-14832-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14832-0

Keywords

Search

Navigation