Earticle

초록

영어

In this paper, an interactive color natural image segmentation method is proposed. The method extends the Gaussian Mixture Model (GMM) by taking into account user markers as useful spatial prior. In this way, a distribution combining with color and spatial location is obtained. The distribution is incorporated in a Bayesian MAP approach. The approach is formalized as an iterative energy minimization problem. A direct global minimization technique based on variational method is employed to get binary solution. After each iteration, the largest connected region from foreground is used to update foreground GMMs and achieve more superior performance than original GrabCut method. Extensive experiments are performed on public benchmark datasets such as GrabCut benchmark, Berkeley segmentation database and Graz benchmark. The results show that the proposed method can extract the object region from the complex background accurately.

목차

Abstract
 1. Introduction
 2. Gaussian Mixture Model (GMM)
 3. GMM with Spatial Prior
  3.1. Distance-Based Segmentation
  3.2. GMM with Spatial Prior
  3.3. Minimization via Convex Optimization
  3.4. Estimation of Parameters
  3.5. Segmentation Algorithm
 4. Experimental Results
  4.1. Comparison Results on GrabCut Benchmark
 5. Conclusion
 References

키워드

저자

• Mo Yan [ National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China ]
• Peng-Lang Shui [ National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China ]

참고문헌

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간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605