skip to main content
10.1145/3638209.3638229acmotherconferencesArticle/Chapter ViewAbstractPublication PagesciisConference Proceedingsconference-collections
research-article

Image Scenario classification using Machine learning

Published:28 February 2024Publication History

ABSTRACT

Image Scenario classification is widespread for many IoT applications. Classifying scenario helps in making proper decisions. The study aims at classifying six different scenarios using a deep neural network algorithm. The proposed InceptionV3 classification algorithm could predict the scenarios and achieve 92.00% accuracy. A quick comparison is shown with the traditional machine learning algorithms, SVM and MLP. The study shows the power of the deep neural algorithm and classifies the scene image dataset with higher precision.

References

  1. Oliva, Aude, and Antonio Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," IJCV, vol. 42, pp. 145-175, 2001.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Kwitt, Roland, Nuno Vasconcelos, and Nikhil Rasiwasia, "Scene recognition on the semantic manifold," ECCV, Springer, pp. 359-372, 2012.Google ScholarGoogle Scholar
  3. Li, Li-Jia, , "Object bank: A high-level image representation for scene classification & semantic feature sparsification," NIPS, pp. 1449-1457, 2010.Google ScholarGoogle Scholar
  4. Lowe, David G., "Distinctive image features from scale-invariant keypoints," IJCC, pp. 91-110, 2004.Google ScholarGoogle Scholar
  5. Jégou, Hervé, , "Aggregating local descriptors into a compact image ´ representation.," CVPR, pp. 3304-3311, 2010.Google ScholarGoogle Scholar
  6. Sánchez, Jorge, , "Image classification with the fisher vector: Theory and practice," IJCV, pp. 222-245, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Zhou, Bolei, , "Learning deep features for scene recognition using places database.," NIPS, pp. 487-495, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Gong, Yunchao, , "Multi-scale orderless pooling of deep convolutional activation features," ECCV, Springer, pp. 392-407, 2014.Google ScholarGoogle Scholar
  9. Liu, Lingqiao, , "Encoding high dimensional local features by sparse coding based fisher vectors," NIPS, pp. 1143-1151, 2014.Google ScholarGoogle Scholar
  10. Hinton, Geoffrey E., and Ruslan R. Salakhutdinov., "Reducing the dimensionality of data with neural networks," Science, vol. 313, pp. 504-507, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  11. Yao, Xiwen, , "“Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning,”," IEEE Trans. Geosci. Remote Sens, vol. 54, pp. 3660-3671, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  12. Zou, Qin, , "Deep learning based feature selection for remote sensing scene classification," IEEE Geosci. Remote Sens. Lett, vol. 12, pp. 2321-2315, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  13. Cheng, Gong, Peicheng Zhou, and Junwei Han., "Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images," IEEE Trans. Geosci. Remote Sens., vol. 53, pp. 7405-7415.Google ScholarGoogle Scholar
  14. Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets,," Neural Comput., vol. 18, pp. 1527-1554, 2006.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Hinton, G., and R. Salakhutdinov, "An efficient learning procedure for deep Boltzmann machines," Neural Comput, vol. 24, pp. 1967-2007, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Vincent, Pascal, , "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res, vol. 11, pp. 3371-3408, 2012.Google ScholarGoogle Scholar
  17. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton., "Imagenet classification with deep convolutional neural networks," Proc. Conf. Adv. Neural Inform. Process. Syst, pp. 1097-1105, 2012.Google ScholarGoogle Scholar
  18. Sermanet, Pierre, , "Overfeat: Integrated recognition, localization, and detection using convolutional networks," Proc. Int. Conf. Learn. Represent, pp. 1-16, 2014.Google ScholarGoogle Scholar
  19. He, Kaiming, , "Deep residual learning for image recognition, " Proc. IEEE Int. Conf. Comput. Vision Pattern Recognit, pp. 770-778, 2016.Google ScholarGoogle Scholar
  20. Yang, Yi, and Shawn Newsam. "Bag-of-visual-words and spatial extensions for land-use classification,," Proc. 18th SIGSPATIAL Int. Conf. Adv. Geograph. Inf. Syst, pp. 270-279, 2010.Google ScholarGoogle Scholar
  21. Xia, Gui-Song, , "Structural high-resolution satellite image indexing," ISPRS TC VII Symposium-100 Years ISPRS, pp. 298-303, 2010.Google ScholarGoogle Scholar
  22. Zou, Qin, , "Deep learning based feature selection for remote sensing scene classification," IEEE Geosci. Remote Sens. Lett.,, pp. 2321-2325, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  23. Penatti, Otávio AB, Keiller Nogueira, and Jefersson A. Dos Santos., "Do deep features generalize from everyday objects to remote sensing and aerial scenes domains," Proc. IEEE Conf. Comput. Vision Pattern Recognit. Workshops,, pp. 44-51, 2015.Google ScholarGoogle Scholar
  24. Zhao, Bei, , "Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens, vol. 54, pp. 2018-2123, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  25. Zhao, Lijun, Ping Tang, and Lianzhi Huo, "Feature significance-based multibag-ofvisual-words model for remote sensing image scene classification," J. Appl. Remote Sens, vol. 10, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  26. Xia, Gui-Song, , "Aid: A benchmark data set for performance evaluation of aerial scene classification," IEEE Trans. Geosci. Remote Sens., vol. 55, pp. 3965-3981, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  27. Cheng, Gong, Junwei Han, and Xiaoqiang Lu., "Remote sensing image scene classification: Benchmark and state-of-the-art,," Proc. IEEE, vol. 105, pp. 1865-1883, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  28. Wang, Qi, , "Scene classification with recurrent attention of VHR remote sensing images," IEEE Trans. Geosci. Remote Sens, vol. 57, pp. 1155-1167, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  29. Helber, Patrick, , "EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens, vol. 12, pp. 2217-2226, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  30. Sumbul, Gencer, "BigEarthNet: A large-scale benchmark archive for remote sensing image understanding," Proc. IEEE Int. Geosci. Remote Sens. Symp, pp. 5901-5904, 2019.Google ScholarGoogle Scholar
  31. Zhang, Fan, Bo Du, and Liangpei Zhang., "Scene classification via a gradient boosting random convolutional network framework,," IEEE Trans. Geosci. Remote Sens, vol. 54, pp. 1793-1802, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  32. Zhong, Yanfei, Feng Fei, and Liangpei Zhang., "Large patch convolutional neural networks for the scene classification of high spatial resolution imagery," J. Appl. Remote Sens, vol. 10, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  33. Chaib, Souleyman, , "Deep feature fusion for VHR remote sensing scene classification,," IEEE Trans. Geosci. Remote Sens, vol. 55, pp. 4775-4784, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  34. He, Nanjun, , "Remote sensing scene classification using multilayer stacked covariance pooling," IEEE Trans. Geosci. Remote Sens., vol. 56, pp. 6899-6910, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  35. Cheng, Gong, , "When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs," IEEE Trans. Geosci. Remote Sens, vol. 57, pp. 1155-1167, 2018.Google ScholarGoogle Scholar
  36. Sun, Hao, , "Remote sensing scene classification by gated bidirectional network," IEEE Trans. Geosci. Remote Sens, vol. 58, pp. 82-96, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  37. Zhu, Ruixi, , "Attention-based deep feature fusion for the scene classification of high-resolution remote sensing images," Remote Sens, vol. 11, pp. 519-531, 1996.Google ScholarGoogle Scholar
  38. He, Nanjun, , "Remote sensing scene classification using multilayer stacked covariance pooling," IEEE Trans. Geosci. Remote Sens, vol. 56, pp. 6899-6910, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  39. Zhang, Wei, Ping Tang, and Lijun Zhao, "Remote sensing image scene classification using CNN-CapsNe," Remote Sens, vol. 11, p. 494, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  40. Xia, Xiaoling, Cui Xu, and Bing Nan. "Inception-v3 for flower classification." 2017 2nd international conference on image, vision and computing (ICIVC). IEEE, 2017.Google ScholarGoogle Scholar

Index Terms

  1. Image Scenario classification using Machine learning

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      CIIS '23: Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems
      November 2023
      193 pages
      ISBN:9798400709067
      DOI:10.1145/3638209

      Copyright © 2023 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 February 2024

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited
    • Article Metrics

      • Downloads (Last 12 months)14
      • Downloads (Last 6 weeks)4

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format