Skip to main content
SpringerLink
Log in

Efficient Deep Learning Approach for Multi-label Semantic Scene Classification

  • Conference paper
  • First Online:
Image Processing and Capsule Networks (ICIPCN 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1200))

Included in the following conference series:

Abstract

Recognition of semantic scene is a tedious task in which a single scene image is correlated with multiple classes. Also, the semantic scene classification is represented as multi instance multi label (MIML) classification, a part of multi label (ML) classification. Identifying the complex semantic relationship between the classes is the major issue in the traditional Machine Learning Technique (MLT) for MIML learning. Therefore, an efficient Deep Learning framework, Convolutional Neural Network (CNN) with a Gaussian blur filter is proposed. The proposed framework supports to identify the complex class correlations between various classes. Experimental results show that the proposed CNN for ML learning scene classification achieves a better predictive performance of 90% and a hamming loss of 0.13 when compared with the existing Machine Learning Techniques (MLT).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Institutional subscriptions

References

  1. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  2. Gao, J., Yang, J., Zhang, J., Li, M.: Natural scene recognition based on convolutional neural networks and deep Boltzmann machines. In: 2015 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 2369–2374. IEEE (2015)

    Google Scholar 

  3. Zhang, Z.L., Zhang, M.L.: Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems, pp. 1609–1616 (2007)

    Google Scholar 

  4. Tong-tong, C., Chan-Juan, L., Hai-Lin, Z., Shu-sen, Z., Ying, L., Xin-miao, D.: A multi-instance multi-label scene classification method based on multi-kernel fusion. In: 2015 SAI Intelligent Systems Conference (IntelliSys), pp. 782–787. IEEE (2015)

    Google Scholar 

  5. McCallum, A.K.: Multi-label text classification with a mixture model trained by EM. In: AAAI 1999 Workshop on Text Learning (1999)

    Google Scholar 

  6. Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2–3), 135–168 (2000)

    Article  Google Scholar 

  7. Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 42–53. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44794-6_4

  8. Campbell, N.W., Mackeown, W.P.J., Thomas, B.T., Troscianko, T.: The automatic classification of outdoor images. In: International Conference on Engineering Applications of Neural Networks, pp. 339–342 (1996)

    Google Scholar 

  9. Shi, X., Manduchi, R.: A study on Bayes feature fusion for image classification. In: 2003 Conference on Computer Vision and Pattern Recognition Workshop, vol. 8, p. 95. IEEE (2003)

    Google Scholar 

  10. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  11. Botalb, A., Moinuddin, M., Al-Saggaf, U.M., Ali, S.S.: Contrasting Convolutional Neural Network (CNN) with Multi-Layer Perceptron (MLP) for big data analysis. In: 2018 International Conference on Intelligent and Advanced System (ICIAS), pp. 1–5. IEEE (2018)

    Google Scholar 

  12. Bautell, M., Luo, J., Gray, R.T.: Sunset scene classification using simulated image recomposition. In: Proceedings of 2003 International Conference on Multimedia and Expo, ICME 2003, (Cat. no. 03TH8698), vol. 1, pp. I-37. IEEE (2003)

    Google Scholar 

  13. Belongie, S., Carson, C., Greenspan, H., Malik, J.: Recognition of Images in Large Databases Using a Learning Framework. University of California at Berkeley, Berkeley (1997)

    Google Scholar 

  14. Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., Pollefeys, M.: Semantic3D.NET: a new large-scale point cloud classification benchmark. arXiv preprint arXiv:1704.03847 (2017)

  15. Lipson, P., Grimson, E., Sinha, P.: Configuration based scene classification and image indexing. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1007–1013. IEEE (1997)

    Google Scholar 

  16. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)

    Article  Google Scholar 

  17. Oliva, A., Torralba, A.: Scene-centered description from spatial envelope properties. In: International Workshop on Biologically Motivated Computer Vision, pp. 263–272. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36181-2_26

  18. Paek, S., Chang, S.F.: A knowledge engineering approach for image classification based on probabilistic reasoning systems. In: Proceedings of 2000 IEEE International Conference on Multimedia and Expo, ICME 2000. Latest Advances in the Fast Changing World of Multimedia (Cat. no. 00TH8532), vol. 2, pp. 1133–1136. IEEE (2000)

    Google Scholar 

  19. Serrano, N., Savakis A., Luo, A.: A computationally efficient approach to indoor/outdoor scene classification. In: Object Recognition Supported by User Interaction for Service Robots, vol. 4, pp. 146–149. IEEE (2002)

    Google Scholar 

  20. Smith, J.R., Li, C.-S.: Image classification and querying using composite region templates. Comput. Vis. Image Underst. 75(1–2), 165–174 (1999)

    Article  Google Scholar 

  21. Song, Y., Zhang, A.: Analyzing scenery images by monotonic tree. Multimed. Syst. 8(6), 495–511 (2003). https://doi.org/10.1007/s00530-002-0067-y

    Article  Google Scholar 

  22. Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42–51. IEEE (1998)

    Google Scholar 

  23. Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413–420. IEEE (2009)

    Google Scholar 

  24. Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Content-based hierarchical classification of vacation images. In: Proceedings IEEE International Conference on Multimedia Computing and Systems, vol. 1, pp. 518–523. IEEE (1999)

    Google Scholar 

  25. Zhou, L., Zhou, Z., Dewen, H.: Scene classification using multi-resolution low-level feature combination. Neurocomputing 122, 284–297 (2013)

    Article  Google Scholar 

  26. Zang, M., Wen, D., Wang, K., Liu, T., Song, W.: A novel topic feature for image scene classification. Neurocomputing 148, 467–476 (2015)

    Article  Google Scholar 

  27. Zhou, L., Zhou, Z., Hu, D.: Scene classification using a multi-resolution bag-of-features model. Pattern Recogn. 46(1), 424–433 (2013)

    Google Scholar 

  28. Gao, J., Yang, J., Wang, G., Li, M.: A novel feature extraction method for scene recognition based on centered convolutional restricted Boltzmann machines. Neurocomputing 214, 708–717 (2016)

    Article  Google Scholar 

  29. Qi, X., Li, C.-G., Zhao, G., Hong, X., Pietikäinen, M.: Dynamic texture and scene classification by transferring deep image features. Neurocomputing 171, 1230–1241 (2016)

    Article  Google Scholar 

  30. Tang, P., Wang, H., Kwong, S.: G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 225, 188–197 (2017)

    Article  Google Scholar 

  31. Li, J., Liang, C., Zhang, B., Wang, Z., Xiang, F., Chu, X.: Neural architecture search on acoustic scene classification. arXiv preprint arXiv:1912.12825 (2019)

  32. Zhang, Y.-D., Dong, Z., Chen, X., Jia, W., Sidan, D., Muhammad, K., Wang, S.-H.: Image-based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimed. Tools Appl. 78(3), 3613–3632 (2019)

    Article  Google Scholar 

  33. Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: Proceedings of the AAAİ Conference on Artificial İntelligence, vol. 33, pp. 4780–4789 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University College of Engineering, Anna University, Tiruchirappalli, India

    D. Senthilkumar & C. Akshayaa

  2. Ramanujan Computing Centre, Anna University, Chennai, India

    D. George Washington

Authors
  1. D. Senthilkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Akshayaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. George Washington
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Akshayaa .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Department of Electronics and Computer Engineering, Tribhuvan University, Lalitpur, Nepal

    Subarna Shakya

  4. College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Abdullah M. Iliyasu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Senthilkumar, D., Akshayaa, C., George Washington, D. (2021). Efficient Deep Learning Approach for Multi-label Semantic Scene Classification. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_37

Download citation

Publish with us

Policies and ethics

Search

Navigation