Qualitative Vocabulary based Descriptor

Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson

2013

Abstract

Creating a single feature descriptors from a collection of feature responses is an often occurring task. As such the bag-of-words descriptors have been very successful and applied to data from a large range of different domains. Central to this approach is making an association of features to words. In this paper we present a new and novel approach to feature to word association problem. The proposed method creates a more robust representation when data is noisy and requires less words compared to the traditional methods while retaining similar performance. We experimentally evaluate the method on a challenging image classification data-set and show significant improvement to the state of the art.

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. Bouachir, W., Kardouchi, M., and Belacel, N. (2009). Improving bag of visual words image retrieval: A fuzzy weighting scheme for efficient indexation. In Proceedings of the 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems, SITIS 7809, pages 215-220, Washington, DC, USA. IEEE Computer Society.
  3. Boureau, Y.-L., Ponce, J., and LeCun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. In ICML, pages 111-118.
  4. Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.
  5. Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009). What is the best multi-stage architecture for object recognition? In Proc. International Conference on Computer Vision (ICCV'09). IEEE.
  6. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, CVPR 7806, pages 2169-2178, Washington, DC, USA. IEEE Computer Society.
  7. Morioka, N. and Satoh, S. (2010). Building compact local pairwise codebook with joint feature space clustering. In Proceedings of the 11th European conference on Computer vision: Part I, ECCV'10, pages 692-705, Berlin, Heidelberg. Springer-Verlag.
  8. Russell, B. C., Efros, A. A., Sivic, J., Freeman, W. T., and Zisserman, A. (2006). Using multiple segmentations to discover objects and their extent in image collections. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  9. Savarese, S., Winn, J., and Criminisi, A. (2006). Discriminative object class models of appearance and shape by correlatons. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, CVPR 7806, pages 2033-2040, Washington, DC, USA. IEEE Computer Society.
  10. Sivic, J., Russell, B. C., Efros, A. A., Zisserman, A., and Freeman, W. T. (2005). Discovering object categories in image collections. In Proceedings of the International Conference on Computer Vision.
  11. Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/.
  12. Wang, X., Mohanty, N., and Mccallum, A. (2005). Group and topic discovery from relations and text. In KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD).
  13. Winn, J., Criminisi, A., and Minka, T. (2005). Object categorization by learned universal visual dictionary. In Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2, ICCV 7805, pages 1800-1807, Washington, DC, USA. IEEE Computer Society.
  14. Zhang, Y. and Chen, T. (2009). Efficient kernels for identifying unbounded-order spatial features. In CVPR, pages 1762-1769.
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Paper Citation


in Harvard Style

Maboudi Afkham H., Henrik Ek C. and Carlsson S. (2013). Qualitative Vocabulary based Descriptor . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 188-193. DOI: 10.5220/0004266901880193


in Bibtex Style

@conference{icpram13,
author={Heydar Maboudi Afkham and Carl Henrik Ek and Stefan Carlsson},
title={Qualitative Vocabulary based Descriptor},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={188-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004266901880193},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Qualitative Vocabulary based Descriptor
SN - 978-989-8565-41-9
AU - Maboudi Afkham H.
AU - Henrik Ek C.
AU - Carlsson S.
PY - 2013
SP - 188
EP - 193
DO - 10.5220/0004266901880193