Image Labeling using Integration of Local and Global Features

Takuto Omiya, Kazuhiro Hotta

Abstract

In this paper, we carry out image labeling based on probabilistic integration of local and global features. Many conventional methods put label to each pixel or region using the features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on a local viewpoint. To overcome this problem, we propose the image labeling method using not only local features but also global features. We compute posterior probability of local and global features independently, and they are integrated by the product. To compute probability of global region (entire image), Bag-of-Words is used. On the other hand, local co-occurrence between color and texture features is used to compute local probability. In the experiments using MSRC21 dataset, labeling accuracy is much improved by using global viewpoint.

References

  1. Arandjelovic, R. Zisserman, A. (2012). Three things everyone should know to improve object retrieval. Proc. Computer Vision and Pattern Recognition, pp. 2911-2918.
  2. Barnard, K. and Forsyth, D. (2001). Learning the semantics of words and pictures. Proc. International Conference on Computer Vision. vol. 2, pp. 408-415.
  3. Chang, C. and Lin, J. (2001). LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/ cjlin/libsvm
  4. Chapelle, O., Haffner, P. and Vapnik, V. (1999). Support vector machines for histogram-based image classification. Neural Networks, vol. 10, pp. 1055- 1064.
  5. Csurka, G., Dance, C., Fan, L., Willamowski, J. and Bray, C. (2004). Visual categorization with bags of keypoints. Proc. ECCV Workshop on Statistical Learning in Computer Vision, pp. 59-74.
  6. Fei-Fei, L. and Perona, P. (2005). A bayesian hierarchical model for learning natural scene categories. Proc. Computer Vision and Pattern Recognition, vol. 2, pp. 524-531.
  7. Galleguillos, C., Rabinovich, A. and Belongie, S. (2008). Object categorization using co-occurrence, location and appearance. Proc. Computer Vision and Pattern Recognition, pp. 1-8.
  8. Gould, S., Rodgers, J., Cohen, D., Elidan, G. and Koller, D. (2008). Multi-class segmentation with relative location prior. International Journal of Computer Vision, vol. 80, pp. 300-316.
  9. Ladicky, L., Russell, C., Kohli, P. and Torr, P. (2010). Graph cut based inference with co-occurrence statistics. Proc. European Conference on Computer Vision. pp. 239-253.
  10. Lafferty, J., McCallum, A. and Pereira, F. (2001). Conditional random fields: probabilistic models for segmenting and labeling sequence data. Proc. International Conference on Machine Learning, pp. 282-289.
  11. Lowe, D. (1999). Object recognition from local scaleivariant features. Proc. International Conference on Computer Vision, vol. 2, pp. 1150-1157.
  12. Nowak, E., Jurie, F. and Triggs, B. (2006). Sampling strategies for bag-of-features image classification. Proc. European Conference on Computer Vision, pp. 490-503.
  13. Ojala, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, vol 24, pp. 971-987.
  14. Shotton, J., Winn, J., Rother, C. and Criminisi, A. (2006). Textonboost: joint appearance, shape and context modelling for multi-class object recognition and segmentation. Proc. European Conference on Computer Vision, pp. 1-15.
  15. Smith, J. and Chang, S. (1996). Tools and techniques for color image retrieval. Symposium on Electronic Imaging: Science and Technoloy-Strage and Retrieval for Image and Video Database IV, pp. 426-437.
  16. Tu, Zhuowen. (2008). Auto-context and its application to high-level vision tasks. Proc Computer Vision and Pattern Recognition, pp. 1-8.
  17. Vapnik, V. (1995). The nature of statistical learning theory, Springer-verlag New York. New York.
  18. Zhang, J., Marzaklek, M., Lazebnik, S. and Schmid, C. (2007). Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision, vol. 73, pp. 213-238.
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Paper Citation


in Harvard Style

Omiya T. and Hotta K. (2013). Image Labeling using Integration of Local and Global Features . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 613-618. DOI: 10.5220/0004334606130618


in Bibtex Style

@conference{icpram13,
author={Takuto Omiya and Kazuhiro Hotta},
title={Image Labeling using Integration of Local and Global Features},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={613-618},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004334606130618},
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 - Image Labeling using Integration of Local and Global Features
SN - 978-989-8565-41-9
AU - Omiya T.
AU - Hotta K.
PY - 2013
SP - 613
EP - 618
DO - 10.5220/0004334606130618