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Interactive Image Segmentation using Graph Cuts. PRASA 2009. Mayuresh Kulkarni and Fred Nicolls Digital Image Processing Group University of Cape Town. Outline. Image Segmentation Problem Our Approach Graph cuts and Gaussian Mixture Models Results and Discussion Future Research.

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interactive image segmentation using graph cuts

Interactive Image Segmentation using Graph Cuts

PRASA 2009

MayureshKulkarni and Fred Nicolls

Digital Image Processing Group

University of Cape Town

outline
Outline
  • Image Segmentation Problem
  • Our Approach
  • Graph cuts and Gaussian Mixture Models
  • Results and Discussion
  • Future Research
our approach
Our Approach

Image properties

eg. colour, texture

Difference between adjacent pixels

8 – pixel neighbourhood

Region information

Boundary information

Pixel connectivity

Graph Cuts Segmentation

Cost Function : E(A) = λ R(A) + B(A)

graph cuts
Graph Cuts

Source (foreground)

Pixel connectivity (boundaries)

Inter-pixel weights (boundaries)

SourceandSinkweights (regions)

Cost Function : E(A) = λ R(A) + B(A)

Sink (background)

gaussian mixture models
Gaussian Mixture Models

Background GMM

Foreground GMM

gaussian mixture models8
Gaussian Mixture Models

Foreground GMM

pf

pb

Log Likelihood Ratio = log(K *pf/pb)

Background GMM

gmm components
GMM components
  • Greyscale images
    • Intensity values
    • Intensity values and MR8 filters
  • Colour images
    • RGB values
    • G, (G-R), (G-B) values
    • Luv values
    • MR8 filters
boundary information
Boundary information
  • Inter-pixel weights
    • Edge detection
    • Difference between adjacent pixels
    • Gradient
  • Pixel connectivity
results
Results

Κ= 0.01

Κ= 0.1

Κ= 1

results12
Results

Original Image

Luv and MR8 (Fscore = 0.921)

Luv (Fscore = 0.934)

RGB, Luv and MR8 (Fscore = 0.916)

results13
Results

Original Image

RGB, Luv and MR8 (Fscore = 0.906)

RGB (Fscore = 0.951)

Luv (Fscore = 0.945)

analysis of results
Analysis of Results
  • Accurate segmentation achieved
  • Components in the GMM depend on image
  • Segmentation can be controlled using K and λ
future research
Future Research
  • Different grid (non-pixel grid)
  • Ratio cuts
  • Exploring other statistical models
  • ObjCut – segmenting particular objects
references
References
  • Y. Boykov and M. P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In ICCV, volume 1, pages 105–112, July 2001.
  • Yuri Boykov and Vladimir Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1124–1137, 2004.
  • PushmeetKohli, Jonathan Rihan, Matthieu Bray, and Philip H. S. Torr. Simultaneous segmentation and pose estimation of humans using dynamic graph cuts. International Journal of Computer Vision, 79(3):285–298, 2008.
  • H. Permuter, J. Francos, and I. Jermyn. Gaussian mixture models of texture and colour for image database. In ICASSP, pages 25–88, 2003.
  • D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. 8th Int’l Conf. Computer Vision, volume 2, pages 416–423, July 2001.
  • Carsten Rother, Vladimir Kolmogorov, and Andrew Blake. “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph., 23(3):309–314, August 2004.