Hypercolumn Model: A Combination Model of Hierarchical Self-Organizing Maps and Neocognitron for Image Recognition

Systems and Computers in Japan

Volumn 31, Issue 2, 2000, Pages 49-61

  Tsuruta, Naoyuki ^a   Taniguchi, Rin Ichiro ^b   Amamiya, Makoto ^b  

^a FUKUOKA UNIVERSITY   (Japan)

^b KYUSHU UNIVERSITY   (Japan)

Author keywords

Hierarchical self organizing map; Hypercolumn model; Image recognition; Manmade neural network; Neocognitron

Indexed keywords

EID: 0347074616     PISSN: 08821666     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1520-684X(200002)31:2<49::AID-SCJ6>3.0.CO;2-8     Document Type: Article

References (21)

1. Fritzke B. Growing cell structures - A self-organizing network for unsupervised and supervised learning. Neural Networks 1994;7:1441-1460.
2. Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 1980;36:193-202.
3. Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 1980;36:193-202.
4. Fukushima K. A neural network model for selective attention in visual pattern recognition. Biol Cybern 1986;55:5-15.
5. Fukushima K. A neural network model for selective attention in visual pattern recognition. Biol Cybern 1986;55:5-15.
6. Fukushima K. Neural networks and information processing. Asakura Publishing; 1989. (in Japanese)
7. Jockusch S, Ritter H. Self-organizing maps: Local competition and evolutionary optimization. Neural Networks 1994;7:1229-1240.
8. Kohonen T. Self-organized function of topologically correct feature maps. Biol Cybern 1988;43:43-69.
9. Kohonen T. Self-organization and associative memory. Springer-Verlag; 1989.
10. Kohonen T. Self-organizing maps. Springer Series in Information Sciences; 1995.
11. Lampinen J, Oja E. Clustering properties of hierarchical self-organizing maps. J Math Imaging Vision 1992;2:261-272.
12. Martinetz T, Schulten K. Topology representing networks. Neural Networks 1994;7:507-522.
13. Oja E, Xu L, Kultanen P. Curve detection by an extended self-organizing map and the related RHT method. Proc Int Neural Network Conf 1990;1:27-30.
14. Rosenblum M, Yacoob Y, Davis L. Human expression recognition from motion using a radial basis function network architecture. IEEE Trans Neural Networks 1996;7:1121-1138.
15. Ruwisch D, Bode M, Purwins H. Parallel hardware implementation of Kohonen's algorithm with an active medium. Neural Networks 1993;6:1147-1158.
16. Sung K, Poggio T. Example-based learning for view-based human face detection. Proc of Image Understanding Workshop 1994;II:843-850.
17. Tsuruta N, Taniguchi R, Amamiya M. The ability of a generalized hypercolumn model for transformation invariant image recognition. Proc of Meeting on Image Recognition and Understanding, 1994, Vol. 1, p 257-264. (in Japanese)
18. Ueda N, Nakano R. A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. Neural Networks 1996;7:1211-1228.
19. Utsugi A. A Bayesian model of topology-preserving map learning. Trans IEICE 1993;J76-D-II:1232-1239.
20. Utsugi A. Lateral interaction in Bayesian self-organizing maps. Trans IEICE 1994;J77-D-II:1329-1336.
21. Weideman WE, Manry MT, Yau H, Gong W. Comparisons of neural network and nearest neighbor classifier via the numeric handprint recognition problem. IEEE Trans Neural Networks 1995;6:1524-1530.