USING ASSOCIATION RULES AND SPATIAL WEIGHTING FOR AN EFFECTIVE CONTENT BASED-IMAGE RETRIEVAL

Ismail Elsayad, Jean Martinet, Thierry Urruty, Taner Danisman, Haidar Sharif, Chabane Djeraba

Abstract

Nowadays, having effective methods for accessing the desired images is essential with the huge amount of digital images. The aim of this paper is to build a meaningful mid-level representation of visual documents to be used later for matching between the query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Then, we represent the spatial constitution of an image as a mixture of n Gaussians in the feature space. Finally, we extract different association rules between frequent visual words in the local context of the image to construct visual phrases. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

References

  1. Agrawal, R., Imielinski, T., and Swami, A. N. (1993). Proceedings of the 1993 acm sigmod international conference on management of data, washington, d.c., may 26-28, 1993. In Buneman, P. and Jajodia, S., editors, Mining Association Rules between Sets of Items in Large Databases. ACM Press.
  2. Baeza-Yates, R. A. and Ribeiro-Neto, B. A. (1999). Modern Information Retrieval. ACM Press / Addison-Wesley.
  3. Bay, H., Ess, A., Tuytelaars, T., and Gool, L. J. V. (2008).
  4. Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346-359.
  5. Belongie, S., Carson, C., Greenspan, H., and Malik, J. (1998). Color- and texture-based image segmentation using the expectation-maximization algorithm and its application to content-based image retrieval. In ICCV, pages 675-682.
  6. Belongie, S., Malik, J., and Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell., 24(4):509- 522.
  7. Bilmes, J. A. (1997). A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models.
  8. Canny, J. (1986). A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679-698.
  9. Chen, X., Hu, X., and Shen, X. (2009). Spatial weighting for bag-of-visual-words and its application in contentbased image retrieval. In PAKDD 7809, pages 867-874.
  10. Fei-Fei, L., Fergus, R., and Perona, P. (2007). Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst., 106(1):59- 70.
  11. Jurie, F. and Triggs, B. (2005). Creating efficient codebooks for visual recognition. In ICCV, pages 604-610.
  12. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  13. Martinet, J. and Satoh, S. (2007). A study of intra-modal association rules for visual modality representation. In CBMI 7807.
  14. Salton, G., Wong, A., and Yang, C. S. (1975). A vector space model for automatic indexing. Commun. ACM, 18(11):613-620.
  15. Sivic, J. and Zisserman, A. (2003). Video google: A text retrieval approach to object matching in videos. In ICCV, pages 1470-1477. IEEE Computer Society.
  16. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In CVPR 2001, volume 1, pages I-511-I-518 vol.1.
  17. Willamowski, J., Arregui, D., Csurka, G., Dance, C. R., and Fan, L. (2004). Categorizing nine visual classes using local appearance descriptors. In In ICPR Workshop on Learning for Adaptable Visual Systems.
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Paper Citation


in Harvard Style

Elsayad I., Martinet J., Urruty T., Danisman T., Sharif H. and Djeraba C. (2010). USING ASSOCIATION RULES AND SPATIAL WEIGHTING FOR AN EFFECTIVE CONTENT BASED-IMAGE RETRIEVAL . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 112-117. DOI: 10.5220/0002836101120117


in Bibtex Style

@conference{visapp10,
author={Ismail Elsayad and Jean Martinet and Thierry Urruty and Taner Danisman and Haidar Sharif and Chabane Djeraba},
title={USING ASSOCIATION RULES AND SPATIAL WEIGHTING FOR AN EFFECTIVE CONTENT BASED-IMAGE RETRIEVAL},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={112-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002836101120117},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - USING ASSOCIATION RULES AND SPATIAL WEIGHTING FOR AN EFFECTIVE CONTENT BASED-IMAGE RETRIEVAL
SN - 978-989-674-028-3
AU - Elsayad I.
AU - Martinet J.
AU - Urruty T.
AU - Danisman T.
AU - Sharif H.
AU - Djeraba C.
PY - 2010
SP - 112
EP - 117
DO - 10.5220/0002836101120117