FAST K-MEANS COLOR IMAGE CLUSTERING WITH NORMALIZED DISTANCE VALUES

Nurdan Akhan BAYKAN; Ali SAGLAM

FAST K-MEANS COLOR IMAGE CLUSTERING WITH NORMALIZED DISTANCE VALUES

Nurdan Akhan BAYKAN, Ali SAGLAM

Öz

Image segmentation is an intermediate image processing stage in which the pixels of the image are grouped into clusters such that the data resulted from this stage is more meaningful for the next stage. Many clustering methods are used widely to segment the images. For this purpose, most clustering methods use the features of the image pixels. While some clustering method consider the local features of images by taking into account the neighborhood system of the pixels, some consider the global features of images. The algorithm of K-means clustering method, that is easy to understand and simple to put into practice, performs by considering the global features of the entire image. In this algorithm, the number of cluster is given by users initially as an input value. For the segmentation, if the distribution of the pixels on a histogram is used, the algorithm runs faster. The values in the histogram must be discrete in a certain range. In this paper, we use the Euclidean distance between the color values of the pixels and the mean color values of the entire image for taking advantage of the every color values of the pixels. To obtain a histogram that consists of discrete values, we normalize the distance value in a specific range and round the values to the nearest integer for discretization. We tested the versions of K-means with the gray level value histogram and the distance value histogram on an urban image dataset getting from ISPRS WG III/4 2D Semantic Labeling dataset. When comparing the two histograms, the distance value histogram that is proposed in this paper is better than the gray level value histogram.

Anahtar Kelimeler

Clustering, image segmentation, K-means, histogram

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Referanslar

de Amorim, R.C., Makarenkov, V., 2016, "Applying subclustering and Lp distance in Weighted K-Means with distributed centroids", Neurocomputing, 173, pp.700–707.

Axelsson, P., 2000, "DEM Generation from Laser Scanner Data Using adaptive TIN Models", International Archives of Photogrammetry and Remote Sensing, 33(Part B4), pp.110–117.

Cheng, H.D. et al., 2001, "Color image segmentation: Advances and prospects", Pattern Recognition, 34(12), pp.2259–2281.

Dai, S. et al., 2015, "A novel approach of lung segmentation on chest CT images using graph cuts", Neurocomputing, 168, pp.799–807. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0925231215007201.

Felzenszwalb, P.F., Huttenlocher, D.P., 2004, "Efficient graph-based image segmentation", International Journal of Computer Vision, 59(2), pp.167–181.

Gingles, C., Celebi, M.E., 2014, "Histogram-Based Method for Effective Initialization of the K-Means Clustering Algorithm". In FLAIRS Conference.

Gonzalez, R.C., Woods, R.E., 2007, Digital Image Processing (3rd Edition), Pearson International Edition.

Jain, A.K., Murty, M.N., Flynn, P.J., 1999, "Data clustering: a review", ACM Computing Surveys, 31(3), pp.264–323.

Labeling, S., Vaihingen, B., 2016, Use of the Stair Vision Library within the ISPRS Use of the Stair Vision Library within the ISPRS 2D,

Likas, A., Vlassis, N., J. Verbeek, J., 2003, "The global k-means clustering algorithm", Pattern Recognition, 36(2), pp.451–461.

Lin, C.-H. et al., 2014, "Fast K-means algorithm based on a level histogram for image retrieval", Expert Systems with Applications, 41(7), pp.3276–3283. Available at: http://www.sciencedirect.com/science/article/pii/S0957417413009299.

Lloyd, S.P., 1982, "Least Squares Quantization in PCM", IEEE Transactions on Information Theory, 28(2), pp.129–137.

Mignotte, M., 2008, "Segmentation by fusion of histogram-based K-means clusters in different color spaces", IEEE Transactions on Image Processing, 17(5), pp.780–787.

Peng, B., Zhang, L., Zhang, D., 2013, "A survey of graph theoretical approaches to image segmentation", Pattern Recognition, 46(3), pp.1020–1038.

Punjab, P., Punjab, P., 2012, "Performance Analysis of Segmentation Techniques", International Journal of Computer Applications, 45(23), pp.18–23.

Rupali, N., Shweta, J., 2014, "Color Image Segmentation With K Means", Saiom publications, 1(5), pp.389–397. Available at: https://www.google.co.id/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CCEQFjAA&url=http://www.saiompublications.com/journal/index.php/ISSN-2347-7547/article/download/169/pdf&ei=MBxlVb49j6G6BIDQgpgH&usg=AFQjCNGLM4Vr8kBeQ.

Saglam, A., Baykan, N.A., 2016, "Sequential image segmentation based on minimum spanning tree representation", Pattern Recognition Letters, p. Available at: http://www.sciencedirect.com/science/article/pii/S0167865516301192.

Tian, M. et al., 2013, "Automatic histogram-based initialization of K-means clustering in CT". In Bildverarbeitung für die Medizin 2013. Springer Berlin Heidelberg, pp. 277–282.