Advertisement

Object Classification from Shape Detection

  • Pragya NagpalEmail author
  • Ankush Mittal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)

Abstract

We evaluate the problem of object detection and classification based on a single model for five diverse classes. The class detection problem is implemented by enabling a method which detects the presence or absence of every shape-based model in every instance of a class. Low-level feature extraction is also performed to facilitate object categorization on one-dimensional information of the dataset. We compute the categorization performance for both the modalities in separate as well as combined representations to produce improved experimental results. The combined obtained solutions provide a classification of the object into the five classes. We have evaluated our approach on the ETHZ dataset and found that it performs with an accuracy of 88.2% in classification based on object detection.

Keywords

Canny edge detection Image classification MPEG-7 Object classification Object detection Shape detection MPEG-7 Structural similarity Template matching 

References

  1. 1.
    Basri, R., Costa, L., Geiger, D., Jacobs, D.: Determining the similarity of deformable shapes. Vision. Res. 38(15–16), 2365–2385 (1998)CrossRefGoogle Scholar
  2. 2.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts (2002)CrossRefGoogle Scholar
  3. 3.
    Gavrila, D.M., & Philomin, V.: Real-time object detection for “smart” vehicles. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 87–93. IEEE (1999)Google Scholar
  4. 4.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: Null, pp. 878–885. IEEE (2005)Google Scholar
  5. 5.
    Thayananthan, A., Stenger, B., Torr, P. H., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings 2003, vol. 1, pp. I–I. IEEE (2003)Google Scholar
  6. 6.
    Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 26–33. IEEE (2005)Google Scholar
  7. 7.
    Nelson, R.C., Selinger, A.: A cubist approach to object recognition. In: Sixth International Conference on Computer Vision, pp. 614–621. IEEE (1998)Google Scholar
  8. 8.
    Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K., Yuille, A.L.: Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4545–4554 (2016)Google Scholar
  9. 9.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  10. 10.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)CrossRefGoogle Scholar
  11. 11.
    Ferrari, V., Jurie, F., Schmid, C.: From images to shape models for object detection. Int. J. Comput. Vision 87(3), 284–303 (2010)CrossRefGoogle Scholar
  12. 12.
    Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7: Multimedia Content Description Interface, vol. 1. Wiley (2002)Google Scholar
  13. 13.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  14. 14.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)CrossRefGoogle Scholar
  15. 15.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  16. 16.
    Wang, Z., Simoncelli, E., Bovik, A.: Multi-scale structural similarity for image quality assessment. In: Asilomar Conference on Signals Systems and Computers, vol. 2, pp. 1398–1402. IEEE (1998)Google Scholar
  17. 17.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Graphic Era Hill UniversityDehradunIndia

Personalised recommendations