AUTOMATIC INITIALIZATION of IMAGE CLUSTERING ALGORITHMS

Clustering is partition of a data set into subsets where each item in assigned subset is similar and different from that of other subsets. K-means and fuzzy c-means (FCM) algorithms are frequently used for clustering of color image. On the other hand, randomly determination of initial cluster centers is one of the most important problems of both algorithms since results to be obtained vary according to initial values of cluster centers. Thus, obtaining different results at each run time reduces reliability of algorithms. One of a typical solution is that number of iterations is increased in order to obtain an accurate result. However, it increases computation cost. A novel solution for initial cluster centers has been proposed in this study where octree algorithm was used. Color images were initially quantized in certain numbers of color vectors depending on level of octree algorithm. Then, means of each quantized color vector set were obtained. The pixel numbers of each pre-subset were sorted and assigned as initial cluster centers. Consequently, cluster centers are selected automatically. As positions of quantized vectors in color space are fixed, a deterministic algorithm has been attained.

Keywords:

K-means, Fuzzy c-means Octree, Color Quantization,

PDF

___

[1] Isa, N. A. M., Salamah, S. A., and Ngah, U. K. (2009). Adaptive fuzzy moving k-means clustering algorithm for image segmentation. IEEE Transactions on Consumer Electronics, 55(4), 2145-2153.
[2] Kim, D. W., Lee, K. H., and Lee, D. (2004). A novel initialization scheme for the fuzzy c-means algorithm for color clustering. Pattern Recognition Letters, 25(2), 227-237.
[3] Dörterler, S., Dumlu, H., Özdemir, D., Temurtaş, H. (2022). Hybridization of k-means and meta-heuristics algorithms for heart disease diagnosis. New Trends in Engineering and Applied Natural Sciences (55-72. ss).
[4] Juang, L. H., and Wu, M. N. (2011). Psoriasis image identification using k-means clustering with morphological processing. Measurement, 44(5), 895-905.
[5] Hrosik, R. C., Tuba, E., Dolicanin, E., Jovanovic, R., and Tuba, M. (2019). Brain image segmentation based on firefly algorithm combined with k-means clustering. Studies Informatics and Control, 28(2), 167-176.
[6] Nitta, G. R., Sravani, T., Nitta, S., and Muthu, B. (2020). Dominant gray level-based k-means algorithm for MRI images. Health and Technology, 10(1), 281-287.
[7] Yao, H., Duan, Q., Li, D., and Wang, J. (2013). An improved k-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling, 58(3-4), 790-798.
[8] Pustokhina, I. V., Pustokhin, D. A., Rodrigues, J. J., Gupta, D., Khanna, A., Shankar, K., and Joshi, G. P. (2020). Automatic vehicle license plate recognition using optimal k-means with convolutional neural network for intelligent transportation systems. Ieee Access, 8, 92907-92917.
[9] Tan, K. S., Lim, W. H., and Isa, N. A. M. (2013). Novel initialization scheme for fuzzy c-means algorithm on color image segmentation. Applied Soft Computing, 13(4), 1832-1852.
[10] Gamino-Sánchez, F., Hernández-Gutiérrez, I. V., Rosales-Silva, A. J., Gallegos-Funes, F. J., Mújica-Vargas, D., Ramos-Díaz, E., and Kinani, J. M. V. (2018). Block-matching fuzzy c-means clustering algorithm for segmentation of color images degraded with Gaussian noise. Engineering Applications of Artificial Intelligence, 73, 31-49.
[11] Demirci, R., Güvenç, U., and Kahraman, H. T. (2014). Görüntülerin renk uzayı yardımıyla ayrıştırılması. İleri Teknoloji Bilimleri Dergisi, 3(1), 1-8.
[12] Hussein, S. (2021). Automatic layer segmentation in H&E images of mice skin based on colour deconvolution and fuzzy c-mean clustering. Informatics in Medicine Unlocked, 25, 100692.
[13] Pérez-Delgado, M. L. (2019). The color quantization problem solved by swarm-based operations. Applied Intelligence, 49(7), 2482-2514.
[14] Park, H. J., and Kim, K. B. (2015). Improved k-means color quantization based on octree. Journal of The Korea Society of Computer and Information, 20(12), 9-14.
[15] Chowdhury, K., Chaudhuri, D., and Pal, A. K. (2021). An entropy-based initialization method of K-means clustering on the optimal number of clusters. Neural Computing and Applications, 33(12), 6965-6982.
[16] Cao, F., Liang, J., and Jiang, G. (2009). An initialization method for the K-Means algorithm using neighborhood model. Computers and Mathematics with Applications, 58(3), 474-483.
[17] Celebi, M. E., Kingravi, H. A., and Vela, P. A.(2013). A comparative study of efficient initialization methods for the K-means clustering algorithm. Expert System with Applications, 40(1), 200-210.
[18] Kılıçaslan, M., Tanyeri, U., İncetaş, M. O., Girgin, B. Y., and Demirci, R. (2017). Eşikleme Tekniklerinin Renk Uzayı Tabanlı Kümeleme Yönteminin Başarısına Etkisi. In 1st International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2017), Tokat, Türkiye (pp. 107-110).
[19] Dursunoğlu, N. (2006). Image compression using vector quantization, M.Sc. thesis, Yıldız Technical University, Graduate School of Science and Engineering, İstanbul, 4-7.
[20] Kalkan, M. B. (2019). Run-length encoding and segmentation based image compression, M.Sc. thesis, Gazi University, Graduate School of Natural and Applied Sciences, Ankara.
[21] MacQueen, J. (1967). Classification and analysis of multivariate observations. In 5th Berkeley Symposium Mathematical Statistics and Probability (pp. 281-297).
[22] Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science and Business Media.
[23] Gervautz, M., and Purgathofer, W. (1988). A simple method for color quantization: Octree quantization. In New Trends in Computer Graphics (pp. 219-231). Springer, Berlin, Heidelberg.
[24] Morton, G. M. (1966). A computer oriented geodetic database and a new technique in file sequencing.
[25] Laurmaa, V., Picasso, M., and Steiner, G. (2016). An octree-based adaptive semi-Lagrangian VOF approach for simulating the displacement of free surfaces. Computers and Fluids, 131, 190-204.
[26] Kılıçaslan, M. (2020). Content based image retrieval by using color histogram, Ph.D. thesis, Gazi University, Graduate School of Natural and Applied Sciences, Ankara, 129s.