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


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.


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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

author={Mouna Dammak and Mahmoud Mejdoub and Mourad Zaied and Chokri Ben Amar},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
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