Abstract
A Markov Random Field based image segmentation system which combines top-down and bottom-up segmentation approaches is proposed in this study. The system is especially proposed for applications where no labeled training set is available, but some priori general information referred as domain specific information about the dataset is available. Domain specific information is received from a domain expert and formalized by a mathematical representation. The type of information and its representation depends on the content of the image dataset to be segmented. This information is integrated to the segmentation process in an unsupervised framework. Due to the inclusion of domain specific information, this approach can be considered as a first step to semantic image segmentation under an unsupervised MRF model. The proposed system is compared with the state of the art unsupervised image segmentation methods quantitatively via two evaluation metrics; consistency error and probabilistic rand index and satisfactory results are obtained.
Chapter PDF
References
Besag, J.: Spatial Interaction and the Statistical Analysis of Lattice Systems. Journal of the Royal Statistical Society, Series B 36(2), 192–236 (1974)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
Christoudias, C., Georgescu, B., Meer, P.: Synergism in low-level vision. In: 16th International Conference on Pattern Recognition, Quebec City, Canada, vol. IV, pp. 150–155 (2002)
Pantofaru, C., Hebert, M.: A Comparison of Image Segmentation Algorithms. The Robotics Institute, Carnegie Mellon University, Number CMU-RI-TR-05-40 (2005)
Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 1124–1131, 20-25 (2005)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6(6), 721–741 (1984)
Kohli, P., Ladicky, L., Torr, P.: Robust higher order potentials for enforcing label consistency. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8, 23-28 (2008)
Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Associative hierarchical CRFs for object class image segmentation. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 739–746 (2009)
Martin, D.R., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Technical Report, EECS Department University of California, Berkeley (2001)
Motohka, T., Nasahara, K.N., Oguma, H., Tsuchida, S.: Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sens. 2, 2369–2387 (2010)
Felzenswalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59(2) (2004)
Dongcai, S.: Efficient Graph Based Image Segmentation Code, http://www.mathworks.com/matlabcentral/fileexchange/29299-efficient-graph-based-image-segmentation
Cour, T., Yu, S., Shi, J.: Normalized Cut Segmentation Code. Copyright 2004 University of Pennsylvania, Computer and Information Science Department (2004)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward Objective Evaluation of Image Segmentation Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 6(29), 929–944 (2007)
Kato, Z., Pong, T.C., Lee, J.C.M.: Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters 22(3-4), 309–321 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Öztimur Karadağ, Ö., Yarman Vural, F.T. (2013). MRF Based Image Segmentation Augmented with Domain Specific Information. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41184-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-41184-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41183-0
Online ISBN: 978-3-642-41184-7
eBook Packages: Computer ScienceComputer Science (R0)