JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Method of Color Image Segmentation Based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) Using Compactness of Superpixels and Texture Information
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Method of Color Image Segmentation Based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) Using Compactness of Superpixels and Texture Information
Lee, Jeonghwan;
  PDF(new window)
 Abstract
In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.
 Keywords
DBSCAN;Superpixel;Color Image Segmentation;SLIC;Compactness and Texture;
 Language
Korean
 Cited by
 References
1.
Feng Ge, Song Wang, and Tiecheng liu, "New Benchmak Image Segmentation Evaluation," Journal of Electronic Imaging, Vol. 16, No. 3, 2007.

2.
S. Makrogiannis, G. Economou, S. Fotopoulos, "A region dissimilarity relation that combines feature-space and spatial information for color image segmentation," IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 35, No. 1, Feb. 2005, pp. 44-53. crossref(new window)

3.
W. Tao, H. Jin, Y. Zhang. "Color Image Segmentation Based on Mean Shift and Normalized Cuts," IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol.37, No. 5, Oct. 2007, pp. 382-1389.

4.
Jong Hyun Park, Guee Sang Lee, Soon Young Park. "Color image segmentation using adaptive mean shift and statistical model-based methods," Computers and Mathematics with Applications. Vol.57, 2009, pp.970-980. crossref(new window)

5.
Zeng Liu, Dong Zhou, Naijun Wu, "Varied Density Based Spatial Clustering of Application with Noise," in proceedings of IEEE Conference ICSSSM, 2007, pp. 528-531.

6.
Hongfang Zhou, Peng Wang, Hongyan Li, "Research on Adaptive Parameters Determination in DBSCAN Algorithm," Journal of Information & Computational Science Vol. 9, No. 7, 2012.

7.
Sheikholeslami G., Chatterjee S., and Zhang A., "WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases," Proc. 24th Int. Conf. on Very Large DataBases, New York, NY, 1998, pp. 428 - 439.

8.
Hattori K., Torii Y. : "Effective algorithms for the nearest neighbor method in the clustering problem," Pattern Recognition, Vol. 26, No. 5, 1993, pp. 741-746. crossref(new window)

9.
H. Tian, H. Cai, J. H. Lai and X. Xu, "Efficetive Image Noise Removal based on Difference Eigenval," Proc. IEEE Conf. on Image Processing, 2011, pp. 3357-3360.

10.
Fan Yang, Huchuan Lu, and Ming-Hsuan, "Robust Superpixel Tracking," IEEE Transaction on Image Processing, Vol. 23, No. 4, 2014, pp. 1639-1651. crossref(new window)

11.
Andrea Vedaldi and Stefano Soatto, "Quick Shift and Kernel Methods for Mode Seeking," Lecture Notes in Computer Science, Vol. 5305, 2008, pp. 705-718.

12.
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, "SLIC Superpixels," EPFL Technical Report 149300, June 2010.

13.
J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Anal. Mach. Intell. Vol. 22, No. 8, 2000, pp.888-905. crossref(new window)

14.
Nocedal, Jorge; Wright, Stephen, "Numerical Optimization," Springer Verlag. ISBN 978-0387987934, 2000.

15.
D. H. P. Felzenszwalb, "Efficient Graph-based Image Segmentation," Journal of Computer Vision, Vol.59, No. 2, 2004, pp. 167-181. crossref(new window)

16.
이정환, "칼라특징공간별 SLIC기반 슈퍼픽셀의 특성비교," 디지털산업정보학회 논문지, 제10권, 제4호, 2014, pp. 151-160.

17.
Peter Kovesi, "Image Segmentation using SLIC SuperPixels and DBSCAN Clustering," http://www.peterkovesi.com/projects/segmentation/index.html, 2013.

18.
이현구, 김동주, "2D-PCA와 영상분할을 이용한 얼굴인식," 디지털산업정보학회 논문지, 제8권, 제2호, 2012, pp. 31-40.

19.
Jeong Hwan Lee, "Texture Characteristics with Eigenvalue and Superpixel Generation," Proceeding of Korea Multimedia Society, Vol. 1, 2015(in korean).

20.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC Superpixel Compared to State-of-the-art Superpixel Method," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, 2012, pp. 2274-2282. crossref(new window)

21.
Jeong Hwan Lee, "Color Image Segmentation Using Compactness of Superpixels," Proceeding of Korea Multimedia Society, Vol. 2, 2015(in korean).