FEATURE VECTOR APPROXIMATION BASED ON WAVELET NETWORK

Mouna Dammak, Mahmoud Mejdoub, Mourad Zaied, Chokri Ben Amar

Abstract

Image classification is an important task in computer vision. In this paper, we propose a new image representation based on local feature vectors approximation by the wavelet networks. To extract an approximation of the feature vectors space, a Wavelet Network algorithm based on fast Wavelet is suggested. Then, the K-nearest neighbor (K-NN) classification algorithm is applied on the approximated feature vectors. The approximation of the feature space ameliorates the feature vector classification accuracy.

References

  1. Amar, C. B., Zaied, M., and Alimi, M. A. (2005). Beta wavelets. synthesis and application to lossy image compression. Advances in Engineering Software, 36:459-474.
  2. Bay, H., Tuytelaars, T., and Gool, L. V. (2006). Surf: Speeded up robust features. In 9th European Conference on Computer Vision.
  3. Bosch, A., Zisserman, A., and Munoz, X. (2008). Scene classification using a hybrid generative/discriminative approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(4):712-727.
  4. Cohen, A., Dahmen, W., Daubechies, I., and Devore, R. (2001). Tree approximation and optimal encoding. Applied and Computational Harmonic Analysis, 11(2):192-226.
  5. Csurka, G., Dance, C. R., Fan, L., Willamowski, J., and Bray, C. (2004). Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV.
  6. Ejbali, R., Zaied, M., and Amar, C. B. (2010). Wavelet network for recognition system of arabic word. International Journal of Speech Technology, 13(3):163-174.
  7. Elkan, C. (2003). Using the triangle inequality to accelerate k-means. ICML, pages 147-153.
  8. Fourier, J. B. J. (1822). Thorie analytique de la chaleur.
  9. Horster, E., Greif, T., Lienhart, R., and Slaney, M. (2008). Comparing local feature descriptors in plsa-based image models. In DAGM-Symposium, pages 446-455.
  10. Jemai, O., Zaied, M., Amar, C. B., and Alimi, M. A. (2010). Fbwn: An architecture of fast beta wavelet networks for image classification. In International Joint Conference on Neural Networks (IJCNN), pages 1-8, Barcelona.
  11. Jemai, O., Zaied, M., Amar, C. B., and Alimi, M. A. (2011). Pyramidal hybrid approach: Wavelet network with ols algorithm-based image classification. International Journal of Wavelets, Multiresolution and Information Processing (IJWMIP), 9(1):111-130.
  12. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 2169-2178, New York.
  13. Li, C., Liao, X., and Yu, J. (2003). Complex-valued wavelet network. Journal of Computer and System Sciences, 67(3):623-632.
  14. Li, J. and Allinson, N. M. (2008). A comprehensive review of current local features for computer vision. Neurocomputing, 71:1771-1787.
  15. Lowe, D. (1999). Object recognition from local scaleinvariant features. In International Conference on Computer Vision, pages 1150-1157, , Corfu, Greece.
  16. Mejdoub, M. and BenAmar, C. (2011). Hierarchical categorization tree based on a combined unsupervisedsupervised classification. In Seventh International Conference on Innovations in Information Technology.
  17. Mejdoub, M., Fonteles, L., BenAmar, C., and Antonini, M. (2008). Fast indexing method for image retrieval using tree-structured lattices. In Content based multimedia indexing CBMI.
  18. Mejdoub, M., Fonteles, L., BenAmar, C., and Antonini, M. (2009). Embedded lattices tree: An efficient indexing scheme for content based retrieval on image databases. Journal of Visual Communication and Image Representation, Elsevier.
  19. Meyer, Y. (1990). Ondelettes et oprateurs I. Actualits Mathmatiques Current Mathematical Topics. Hermann, Paris.
  20. Morlet, J., Arens, G., Fourgeau, E., and Giard, D. (1982). Wave propagation and sampling theory. Geophysics, 47:203-236.
  21. Mouret, M., Solnon, C., and Wolf, C. (2009). Classification of images based on hidden markov models. In IEEE Workshop on Content Based Multimedia Indexing, pages 169-174.
  22. Nowak, E., Jurie, F., and Triggs, B. (2006). Sampling strategies for bag-of-features image classification. In European Conference on Computer Vision. Springer.
  23. Oliva, A. and Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175.
  24. Piro, P., R.Nock, Nielsen, F., and Barlaud, M. (2010). Boosting k-nn for categorization of natural scenes. CoRR.
  25. Tao, Y., Skubic, M., Han, T., Xia, Y., and Chi, X. (2010). Performance evaluation of sift-based descriptors for object recognition. In Prooceeding of The International MultiConference of Engineers and Computer Scientists.
  26. Wang, J. Z., Li, J., and Wiederhold, G. (2001). Simplicity : Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947-963.
  27. Yan, R. and Gao, R. (2009). Base wavelet selection for bearing vibration signal analysis. International Journal of Wavelets, Multi-resolution, and Information Processing, 7(4):411-426.
  28. Zhang, Q. and Benveniste, A. (1992). Wavelet networks. IEEE Transactions on Neural Networks, 3(6):889- 898.
Download


Paper Citation


in Harvard Style

Dammak M., Mejdoub M., Zaied M. and Ben Amar C. (2012). FEATURE VECTOR APPROXIMATION BASED ON WAVELET NETWORK . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 394-399. DOI: 10.5220/0003776803940399


in Bibtex Style

@conference{icaart12,
author={Mouna Dammak and Mahmoud Mejdoub and Mourad Zaied and Chokri Ben Amar},
title={FEATURE VECTOR APPROXIMATION BASED ON WAVELET NETWORK},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={394-399},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003776803940399},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - FEATURE VECTOR APPROXIMATION BASED ON WAVELET NETWORK
SN - 978-989-8425-95-9
AU - Dammak M.
AU - Mejdoub M.
AU - Zaied M.
AU - Ben Amar C.
PY - 2012
SP - 394
EP - 399
DO - 10.5220/0003776803940399