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Reseach Article

Interactive Image Segmentation using Color and Texture Features

by Sajan Kor, Pramod Kumar Sethy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 9
Year of Publication: 2016
Authors: Sajan Kor, Pramod Kumar Sethy
10.5120/ijca2016908578

Sajan Kor, Pramod Kumar Sethy . Interactive Image Segmentation using Color and Texture Features. International Journal of Computer Applications. 136, 9 ( February 2016), 37-41. DOI=10.5120/ijca2016908578

@article{ 10.5120/ijca2016908578,
author = { Sajan Kor, Pramod Kumar Sethy },
title = { Interactive Image Segmentation using Color and Texture Features },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 9 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number9/24185-2016908578/ },
doi = { 10.5120/ijca2016908578 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:41.004477+05:30
%A Sajan Kor
%A Pramod Kumar Sethy
%T Interactive Image Segmentation using Color and Texture Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 9
%P 37-41
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation is the process of selecting objects of interest in background images. Since the fully automatic segmentation is difficult for natural images due to the complex color and texture features. This paper presents the idea of image segmentation using maximum similarity based region merging with minimum user inputs using simple brush strokes called markers(object and background markers).Therefore, the proposed system presents a new region merging method that grow regions from foreground/background seeds based on the color and texture features for interactive image segmentation. An initial segmentation is required to partition the image into homogeneous regions for merging. After the completion of the initial segmentation, initial segmented regions are represented by means of some descriptors such as color and texture to guide the region merging process. Therefore, the proposed method is very effective and it can quickly and accurately segment a wide variety of natural images with ease.

References
  1. Kailath, Thomas. "The divergence and Bhattacharyya distance measures in signal selection." Communication Technology, IEEE Transactions on 15.1 (1967): 52-60.
  2. Ning, Jifeng, et al. "Interactive image segmentation by maximal similarity based region merging." Pattern Recognition 43.2 (2010): 445-456.
  3. Felzenszwalb, Pedro F., and Daniel P. Huttenlocher. "Efficient graph-based image segmentation." International Journal of Computer Vision 59.2 (2004): 167-181.
  4. Boykov, Yuri, and Vladimir Kolmogorov. "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision." Pattern Analysis and Machine Intelligence, IEEE Transactions on 26.9 (2004): 1124-1137.
  5. Çiğla, Cevahir. "Efficient graph-based image segmentation via speeded-up turbo pixels." Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.
  6. Vezhnevets, Vladimir, and Vadim Konouchine. "GrowCut: Interactive multi-label ND image segmentation by cellular automata." proc. of Graphicon. 2005.
  7. Yuan, Ye, and Chuanjiang He. "Variational level set methods for image segmentation based on both L2 and Sobolev gradients." Nonlinear Analysis: Real World Applications 13.2 (2012): 959-966.
  8. EDISON software. http://www.caip.rutgers.edu/riul/research/code.html.
  9. Bhargava, Neeraj, et al. "Iterative Region Merging and Object Retrieval Method Using Mean Shift Segmentation and Flood Fill Algorithm." Advances in Computing and Communications (ICACC), 2013 Third International Conference on. IEEE, 2013.
  10. Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, “Lazy snapping,” in ACM Siggraph, 2004, pp. 303–308.
  11. Jha, Sonu Kumar, Purnendu Bannerjee, and Subhadeep Banik. "Random Walks based Image Segmentation Using Color Space Graphs." Procedia Technology 10 (2013): 271-278.
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation maximum similarity Region Merging Mean Shift