Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering 


Vol. 43,  No. 1, pp. 20-23, Jan.  2018
10.7840/kics.2018.43.1.20


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  Abstract

In this paper, we propose an adaptive method to sample initial cluster centers for Simple Linear Iterative Clustering (SLIC). The proposed method determines initial cluster centers to be different from adjacent initial cluster centers. Note that this is different from the original SLIC method that assigns the initial cluster centers in a grid form. Our initial cluster centers improves the homogeneity of each superpixel. This yields better adherence to image boundaries. We use the BSD500 dataset to evaluate performance comparison. The proposed method achieves the higher performance with negligible additional computation time. We believe that this study guides for improving superpixel segmentation.

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  Cite this article

[IEEE Style]

H. Eun, Y. Kim, C. Jung, C. Kim, "Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 1, pp. 20-23, 2018. DOI: 10.7840/kics.2018.43.1.20.

[ACM Style]

Hyunjun Eun, Yoonhyung Kim, Chanho Jung, and Changick Kim. 2018. Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering. The Journal of Korean Institute of Communications and Information Sciences, 43, 1, (2018), 20-23. DOI: 10.7840/kics.2018.43.1.20.

[KICS Style]

Hyunjun Eun, Yoonhyung Kim, Chanho Jung, Changick Kim, "Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 1, pp. 20-23, 1. 2018. (https://doi.org/10.7840/kics.2018.43.1.20)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880