Visual feature extraction using variable map-dimension hypercolumn model

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 845-851

  Aly, Saleh ^a   Tsuruta, Naoyuki ^b   Taniguchi, Rin Ichiro ^a   Shimada, Atsushi ^a  

^a KYUSHU UNIVERSITY   (Japan)

^b FUKUOKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; FACE RECOGNITION; HIERARCHICAL SYSTEMS; IMAGE RECOGNITION; MAPS; NEURAL NETWORKS; SPEECH RECOGNITION; VEGETATION;

FEATURE INTEGRATIONS; HIERARCHICAL MODELS; INVARIANT FEATURES; NEURAL NETWORK MODELS; ORL FACE DATABASES; RECOGNITION RATES; SELF-ORGANIZING MAPS; TRAINING DATUMS; VARIABLE DIMENSIONS; VISUAL CORTICES; VISUAL FEATURE EXTRACTIONS;

FEATURE EXTRACTION;

EID: 56349156093     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4633896     Document Type: Conference Paper

References (14)

1. B. Heisele, T. Serre, M. Pontil, T. Vetter, and T. Poggio, Categorization by learning and combining object parts, Advances in Neural. Information Processing Systems, vol. 14, 2002.
2. K. Fukushima, Neocognitron, Biological Cybernetics, vol. 36, no. 4, pp. 193-202, 1980.
3. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio , Robust object recognition with cortex-like mechanisms, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 411-426, 2007.
4. D. Hubel and T. Wiesel, Receptive fields, binocular interaction and functional architecture in the cats visual cotex J. of Physiology, vol 160, pp. 105-154, 1962.
5. N. Tsuruta, R. Taniguchi and M. Amamiya, Hyper column Model: A Combination Model Hierarchical Self- Organizing Maps and Neocognitron for Image Recognition, System and Computer in Japan, vol. 31, no. 2, pp. 49-61, 2000.
6. T. Kohonen, Self-Organizing Maps, Springer, 1997.
7. H. Bauer, K. Pawelzlk, Quantifying the Neighborhood Preservation of Self-Organizing Feature Maps, IEEE Trans. Neural Neworks, vol. 3, no. 4, pp. 570-579, 1992.
8. F. Camastra, Data dimensionality estimation methods: a survey, Pattern Recognition, vol. 36, pp. 2945-2954, 2003.
9. H. Speckmann, G. Raddatz, and W.Rosentiel Relations between generalized fractal dimensions and kohonen self-organizing map in Proc. of NEURONIMES, 1994.
10. S. Peres, M. Netto Fractal fuzzy decision-making: what is the adequate dimension for self-organizing maps, Processing NAFIPS apos,04. IEEE Annual Meeting of the Fuzzy Information. Vol 2, pp. 652-657, 2004.
11. J. Lampinen and E. Oja, Clustering Properties of Hierarchical Self Organizing Maps, Journal of Math. Imaging and Vision, vol. 2, no 2-3, pp. 261-272, 1992.
12. C.R. Tolle, T.R Mc Junkin, D.J. Gorisch, Suboptimal minimum cluster volume cover-based method for measuring fractal dimension, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp.32-41, 2003.
13. P. Grassberger, I., Procaccia, Measuring the strangeness of strange attractors Physics D 9 , pp. 189-208, 1983.
14. F. Sammaria,and A. Harter Parameterization of a stochastic model for human face identification In 2nd IEEE workshop on applications of computer vision, Sarasota, FL, 1994.