Gradual Improvement of Image Descriptor Quality

Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson

Abstract

In this paper, we propose a framework for gradually improving the quality of an already existing image descriptor. The descriptor used in this paper (Afkham et al., 2013) uses the response of a series of discriminative components for summarizing each image. As we will show, this descriptor has an ideal form in which all categories become linearly separable. While, reaching this form is not feasible, we will argue how by replacing a small fraction of these components, it is possible to obtain a descriptor which is, on average, closer to this ideal form. To do so, we initially identify which components do not contribute to the quality of the descriptor and replace them with more robust components. Here, a joint feature selection method is used to find improved components. As our experiments show, this change directly reflects in the capability of the resulting descriptor in discriminating between different categories.

References

  1. Afkham, H. M., Carlsson, S., and Sullivan, J. (2012). Improving feature level likelihoods using cloud features. In ICPRAM (2), pages 431-437.
  2. Afkham, H. M., Ek, C. H., and Carlsson, S. (2013). Qualitative Vocabulary Based Descriptor. In International Conference on Pattern Recognition Applications and Methods, pages 1-6.
  3. Csurka, G. and Perronnin, F. (2011). Fisher Vectors: Beyond Bag-of-Visual-Words Image Representations. In Richard, P. and Braz, J., editors, Computer Vision, Imaging and Computer Graphics. Theory and Applications, pages 28-42. Springer Berlin Heidelberg.
  4. Dalal, N. and Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. In CVPR (1), pages 886-893.
  5. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. (2008). LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871-1874.
  6. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object Detection with Discriminatively Trained Part-Based Models. PAMI, 32(9):1627-1645.
  7. Kumar, M. P., Packer, B., and Koller, D. (2010). Self-Paced Learning for Latent Variable Models. In Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R. S., and Culotta, A., editors, Advances in Neural Information Processing Systems 23, pages 1189-1197.
  8. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In CVPR, pages 2169-2178.
  9. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  10. Madry, M., Afkham, H. M., Ek, C. H., Carlsson, S., and Kragic, D. (2013). Extracting Essential Local Object Characteristics for 3D Object Categorization. In IEEE International Conference on Intelligent Robots and Systems (IROS).
  11. Morioka, N. and Satoh, S. (2010). Building Compact Local Pairwise Codebook with Joint Feature Space Clustering. In ECCV (1), pages 692-705.
  12. Savarese, S., Winn, J., and Criminisi, A. (2006). Discriminative Object Class Models of Appearance and Shape by Correlatons. In CVPR.
  13. Schroff, F., Criminisi, A., and Zisserman, A. (2008). Object Class Segmentation using Random Forests. In Proceedings of the British Machine Vision Conference, pages 54.1-54.10. BMVA Press.
  14. Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An Open and Portable Library of Computer Vision Algorithms. Technical report.
  15. Viola, P. and Jones, M. (2001). Robust real-time face detection. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, page 747.
  16. Winn, J., Criminisi, A., and Minka, T. (2005). Object Categorization by Learned Universal Visual Dictionary. In ICCV.
  17. Yang, W., Wang, Y., Vahdat, A., and Mori, G. (2012). Kernel Latent SVM for Visual Recognition. In Bartlett, P., Pereira, F. C. N., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 818-826.
  18. Zhang, Y. and Chen, T. (2009). Efficient kernels for identifying unbounded-order spatial features. In CVPR.
Download


Paper Citation


in Harvard Style

Maboudi Afkham H., Henrik Ek C. and Carlsson S. (2014). Gradual Improvement of Image Descriptor Quality . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 233-238. DOI: 10.5220/0004826402330238


in Bibtex Style

@conference{icpram14,
author={Heydar Maboudi Afkham and Carl Henrik Ek and Stefan Carlsson},
title={Gradual Improvement of Image Descriptor Quality},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={233-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004826402330238},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Gradual Improvement of Image Descriptor Quality
SN - 978-989-758-018-5
AU - Maboudi Afkham H.
AU - Henrik Ek C.
AU - Carlsson S.
PY - 2014
SP - 233
EP - 238
DO - 10.5220/0004826402330238