Indoor Object Classification Using Higher Dimensional MPEG Features

  • Dibyendu Roy ChaudhuriEmail author
  • Dhairya Chandra
  • Ankush Mittal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)


We propose a generic model to classify a given image by detecting a specific image patch. Employing MPEG-7 features along with feature selection populates the feature space which is used to train using SVM classier. Our work target toward classifying objects in an unclassified image. We propose a model that can detect objects through generic framework for larger classes. Our model gives an overall accuracy of over 97%.


Image classification Object detection MPEG SVM 


  1. 1.
    Bastan, M., Cam, H., Gudukbay, U., Ulusoy, O.: BilVideo-7: an MPEG-7 compatible video indexing and retrieval system. IEEE MultiMedia 17(3), 62–73 (2010)CrossRefGoogle Scholar
  2. 2.
    Marc’Aurelio Ranzato, F.J.H., Boureau, Y.-L., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007Google Scholar
  3. 3.
    Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: 2011 International Conference on Document Analysis and Recognition, 03 Nov 2011Google Scholar
  4. 4.
    Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1) (1998)CrossRefGoogle Scholar
  5. 5.
    Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control, Automation, Robotics Vision, Marina Bay Sands, Singapore, 10–12th Dec 2014 (ICARCV 2014)Google Scholar
  6. 6.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. IEEE Compute SocietyGoogle Scholar
  7. 7.
    Al-doski, J., Mansor1, S.B., Shafri, H.Z.M.: Image classification in remote sensing. J. Environ. Earth Sci. 3(10) (2013). ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)Google Scholar
  8. 8.
    Gavrila, D.M., Philomin, V.: Real-time object detection for “smart” vehicles. In: Proceedings of the Seventh IEEE International Conference on Computer VisionGoogle Scholar
  9. 9.
    Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)CrossRefGoogle Scholar
  10. 10.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, pp. 1–8, Oct 2007. IEEEGoogle Scholar
  11. 11.
    Griffin, G., Holub, A., Perona. P.: Caltech 256 object category dataset. Technical Report UCB/CSD-04-1366, California Institute of Technology (2007)Google Scholar
  12. 12.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  13. 13.
    Theodoridis, S.: Pattern recognition, p. 203. Elsevier B. V (2008). ISBN 9780080949123Google Scholar
  14. 14.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dibyendu Roy Chaudhuri
    • 1
    Email author
  • Dhairya Chandra
    • 1
  • Ankush Mittal
    • 1
  1. 1.Department of Computer ScienceGraphic Era Deemed to Be UniversityDehradunIndia

Personalised recommendations