This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Spatially Varying Color Distributions for Interactive Multilabel Segmentation
May 2013 (vol. 35 no. 5)
pp. 1234-1247
C. Nieuwenhuis, Fac. of Comput. Sci., Tech. Univ. of Munich, Garching, Germany
D. Cremers, Fac. of Comput. Sci., Tech. Univ. of Munich, Garching, Germany
We propose a method for interactive multilabel segmentation which explicitly takes into account the spatial variation of color distributions. To this end, we estimate a joint distribution over color and spatial location using a generalized Parzen density estimator applied to each user scribble. In this way, we obtain a likelihood for observing certain color values at a spatial coordinate. This likelihood is then incorporated in a Bayesian MAP estimation approach to multiregion segmentation which in turn is optimized using recently developed convex relaxation techniques. These guarantee global optimality for the two-region case (foreground/background) and solutions of bounded optimality for the multiregion case. We show results on the GrabCut benchmark, the recently published Graz benchmark, and on the Berkeley segmentation database which exceed previous approaches such as GrabCut [32], the Random Walker [15], Santner's approach [35], TV-Seg [39], and interactive graph cuts [4] in accuracy. Our results demonstrate that taking into account the spatial variation of color models leads to drastic improvements for interactive image segmentation.
Index Terms:
interactive systems,Bayes methods,estimation theory,graph theory,image colour analysis,image segmentation,color models,spatially varying color distributions,interactive multilabel image segmentation,spatial location,generalized Parzen density estimator,spatial coordinate,Bayesian MAP estimation approach,multiregion segmentation,convex relaxation techniques,two-region case,multiregion case,GrabCut benchmark,Graz benchmark,Berkeley segmentation database,Image color analysis,Image segmentation,Joints,Motion segmentation,Kernel,Probability distribution,Bayesian methods,convex optimization,Image segmentation,spatially varying,color distribution
Citation:
C. Nieuwenhuis, D. Cremers, "Spatially Varying Color Distributions for Interactive Multilabel Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1234-1247, May 2013, doi:10.1109/TPAMI.2012.183
Usage of this product signifies your acceptance of the Terms of Use.