Self-Organizing Fusion Neural Networks
Jung-Hua Wang, Chun-Shun Tseng, Sih-Yin Shen,
and Ya-Yun Jheng
Electrical Engineering Department, National Taiwan Ocean University, 2 Peining Rd. Keelung, Taiwan
This paper presents a self-organizing fusion neural network (SOFNN) effective in performing fast clustering and segmentation. Based on a counteracting learning scheme, SOFNN employs two parameters that together control the training in a counteracting manner to obviate problems of over-segmentation and under-segmentation. In particular, a simultaneous region-based updating strategy is adopted to facilitate an interesting fusion effect useful for identifying regions comprising an object in a self-organizing way. To achieve reliable merging, a dynamic merging criterion based on both intra-regional and inter-regional local statistics is used. Such extension in adjacency not only helps achieve more accurate segmentation results, but also improves input noise tolerance. Through iterating the three phases of simultaneous updating, self-organizing fusion, and extended merging, the training process converges without manual intervention, thereby conveniently obviating the need of pre-specifying the terminating number of objects. Unlike existing methods that sequentially merge regions, all regions in SOFNN can be processed in parallel fashion, thus providing great potentiality for a fully parallel hardware implementation.
and Ya-Yun Jheng, “Self-Organizing Fusion Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.6, pp. 610-619, 2007.
-  F. Hoppner, F. Klawonn, R. Kruse, and T. Runkler, “Fuzzy Cluster Analysis,” New York, Wiley, 1999.
-  A. W. C. Liew, H. Yan, and N. F. Law, “Image Segmentation Based on Adaptive Cluster Prototype Estimation,” IEEE Trans. Fuzzy Systems, 13-4, Aug., 2005.
-  A. M. Bensaid, L. O. Hall, J. C. Bezdek, L. P. Clarke, M. L. Silbiger, J. A. Arrington, and R. F. Murtagh, “Validity-Guided (Re)Clustering with Applications to Image Segmentation,” IEEE Trans. Fuzzy Systems, 4-2, May, 1996.
-  C. W. Chen, J. Luo, and K. J. Parker, “Image Segmentation via Adaptive K-Mean Clustering and Knowledge-Based Morphological Operations with Biomedical Applications,” IEEE Trans. Image Processing, 7-12, Dec., 1998.
-  S. A. Hojjatoleslami and J. Kitter, “Region Growing: A New Approach,” IEEE Trans. Image Processing, 7-7, pp. 1079-1084, July, 1998.
-  R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 2nd Ed., Prentice-Hall, 2002.
-  D. Hagyard, M. Razaz, and P. Atkin, “Analysis of Watershed Algorithms for Greyscale Images,” IEEE Proc. Int. Conf. Image Processing, pp. 41-44, 1996.
-  L. Vincent and P. Soille, “Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations,” IEEE Trans. Pattern Anal. Machine Intell., 13-6, pp. 583-598, June, 1991.
-  T. Pavlidis, “Structural Pattern Recognition,” New York, Springer, 1980.
-  K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, “Hybrid image segmentation using watersheds and fast region merging,” IEEE Trans. Image Process., 7, (12), pp. 1684-1699, 1998.
-  A. P. Mendonca and E. A. B. da Silva, “Segmentation Approach Using Local Image Statistics,” Electronics Letters, 36, 14, pp. 1199-1201, July 6, 2000.
-  T. Kohonen, “The Self-Organizing Map,” Proc. of IEEE, 78, 9, pp. 1464-1480, 1990.
-  R. Nock and F. Nielsen, “Statistical Region Merging,” IEEE Trans. Pattern Analysis and Machine Intelligence, 26, 11, pp. 1452-1458, Nov., 2004.
-  C. T. Zahn, “Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters,” IEEE Trans. on Computers, 20, pp. 68-86, Jan., 1971.
-  C. F. Bazlamacci and K. S. Hindi, “Minimum-weight Spanning Tree Algorithms – A Survey and Empirical Study,” Computers & Operations Research, 28, pp. 767-785, 2001.
-  K. A. Ross and C. R. B. Wright, “Discrete Mathematics,” Prentice-Hall, New Jersey, 1999.
-  S. Y. Leung, X. Chen, K. M. Chu, S. T. Yuen, J. Mathy, J. Ji, A. S. Chan, R. Li, S. Law, O. G. Troyanskaya, I. P. Tu, J. Wong, S. So, D. Botstein, and P. O. Brown, “Phospholipase A2 group IIA Expression in Gastric Adenocarcinoma is Associated with Prolonged Survival and Less Frequent Metastasis,” Proc. of the National Academy of Science, 99, 25, pp. 16203-16208, Dec., 2002.
-  D. Jiang, C. Tang, and A. Zhang, “Cluster Analysis for Gene Expression Data: A Survey,” IEEE Trans. Knowledge and data engineering, 16-11, Nov., 2004.
-  B. Zeidman, “Designing with FPGAs and CPLDs,” CMP Books, Sep., 2002.
-  B. Fritzke, “Growing Cell Structure: A Self-organizing Network for Unsupervised and Supervised Learning,” Neural Networks, 7-9, pp. 1441-1460, 1994.
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