Abstract
A novel approach for the classification of image signals for image retrieval using Gradient-Based Fuzzy C-Means with Mercer Kernel (GBFCM-MK) is proposed and presented in this paper. The proposed classifier is a FCM-based algorithm which utilizes the Mercer Kernel to exploit the statistical nature of the image data to improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification algorithm outperforms 21.7% - 24% in accuracy in comparison with conventional algorithms such as the traditional Fuzzy C-Means (FCM), Gradient-based Fuzzy C-Means (GBFCM), and GBFCM with Divergence Measure (GBFCM(DM).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: The Self-Organizing Map. Proc. IEEE 78, 1464–1480 (1990)
Hartigan, J.: Clustering Algorithms. Wiley, New York (1975)
Park, D.C., Dagher, I.: Gradient Based Fuzzy c-means (GBFCM) Algorithm. In: IEEE Int. Conf. on Neural Networks, ICNN 1994, pp. 1626–1631 (1994)
Windham, M.P.: Cluster Validity for the Fuzzy cneans clustering algorithm. IEEE Trans. Pattern Anal. Mach. Int., 357–363 (1982)
Huong, V.T.L., Park, D.C., Woo, D.M.: Image Classification Using Gradient-based Fuzzy C-Means with Divergence Measure. In: IEEE Int. Joint Conf. on Neural Networks, Hong Kong (June 2008 ) (Accepted for presentation)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Looney, C.: Pattern Recognition Using Neural Networks, pp. 252–254. Oxford University press, New York (1997)
Park, D.-C., Tran, C.N., Min, B.-J., Park, S.: Modeling and Classification of Audio Signals Using Gradient-based FCM Algorithm with a Mercer Kernel. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 1104–1108. Springer, Heidelberg (2006)
Daugman, J.G.: Complete Discrete 2D Gabor Transform by Neural Networks for Image Analysis and Compression. IEEE Trans. Acoust., Speech, Signal Process 36, 1169–1179 (1988)
Pun, C.M., Lee, M.C.: Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes. IEEE Trans. Pattern Anal. Mach. Intel. 26(9), 1228–1233 (2004)
Huang, Y.L., Chang, R.F.: Texture Features for DCT-Coded Image Retrieval and Classification. In: ICASSP Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3013–3016 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, DC. (2008). Classification of Image Data Using Gradient-Based Fuzzy C-Means with Mercer Kernel. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-85984-0_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
eBook Packages: Computer ScienceComputer Science (R0)