Abstract
We present an optimal approach to unsupervised color image clustering, suited for high resolution images based on mode seeking by mediod shifts. It is shown that automatic detection of total number of clusters depends upon overall image statistics as well as the bandwidth of the underlying probability density function. An optimized adaptive mode seeking algorithm based on reverse parallel tree traversal is proposed. This work has contribution in three aspects. 1) Adaptive bandwidth for kernel function is proposed based on the overall image statistics; 2) A novel reverse parallel tree traversing approach for mode seeking is presented which drastically reduces number of computational steps as compared to traditional tree traversing. 3) For high resolution images block clustering based optimized Adaptive Mediod Shift (AMS) is proposed where mode seeking is done in blocks and then the local modes are merged globally. The proposed method has made it possible to perform clustering on variety of high resolution images. Experimental results have shown our algorithm time efficient and robust.
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References
Xu, R., Wunsch II, D.: Survey of Clustering Algorithms. IEEE Transactions on Neural Networks 16(3) (May 2005)
Cherkassky, V., Mulier, F.: Learning From Data: Concepts, Theory,and Methods. Wiley, New York (1998)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford Univ. Press, New York (1995)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Fukunaga, K., Hostler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on IT IT-21(1), 32–40 (1975)
Cheng, Y.: Mean shift, mode seeking, and clustering. PAMI 17(8), 790–799 (1995)
Comaniciu, D., Peter, M.: Mean shift: A robust approach toward feature space analysis. PAMI 24(5), 603–619 (2002)
Sheikh, Y.A., Khan, E.A., Kanade, T.: Mode seeking by medoidshift. IEEE Trans., ICCV, 1–8 (2007)
Asghar, A., Rao, N.I.: Color Image Segmentation Using Multilevel Clustering Approach DICTA, Canberra, pp. 519–524 (December 2008)
Asghar, A., Rao, N.I.: Semantics Sensitive Segmentation and Annotation of Natural Images. In: 2008 IEEE International Conference on SITIS, Indonesia, pp. 387–394 (2008)
Breiman, L., Meisel, W., Purcell, E.: Variable kernel estimates of multivariate densities. Technometrics 19, 135–144 (1977)
Georgescu, B., Shimshoni, I., Peter, M.: Mean shift based clustering in high dimensions: A texture classification example. IEEE Trans., ICCV (2003)
Vedaldi, A., Soatto, S.: Quick shift and kernel methods for mode seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)
Garcia, A.L.: Probability and Random Processes for Electrical Engineers, ch. 5.2, 2nd edn. Pearson Education, London
Thomas, H.C.: Introduction to Algorithms, ch. 6, 2nd edn. MIT Press, Cambridge (2001)
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Gilani, Z., Rao, N.I. (2009). Fast Block Clustering Based Optimized Adaptive Mediod Shift. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_53
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DOI: https://doi.org/10.1007/978-3-642-03767-2_53
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