Abstract
The contribution describes a statistical framework for image segmentation that is characterized by the following features: It allows to model scalar as well as multi-channel images (color, texture feature sets, depth, ...) in a region-based manner, including a Gibbs-Markov random field model that describes the spatial (and temporal) cohesion tendencies of ’real’ label fields. It employs a principled target function resulting from a statistical image model and maximum-a-posteriori estimation, and combines it with a computationally very efficient way (’contour relaxation’) for determining a (local) optimum of the target function. We show in many examples that even these local optima provide very reasonable and useful partitions of the image area into regions. A very attractive feature of the proposed method is that a reasonable partition is reached within some few iterations even when starting from a ’blind’ initial partition (e.g. for ’superpixels’), or when — in sequence segmentation — the segmentation result of the previous image is used as starting point for segmenting the current image.
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Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Suesstrunk, S.: SLIC Superpixels. Tech. Rep. Nr. 149300, EPFL, Lausanne (CH) (June 2010)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. PAMI 23(11), 1222–1239 (2002)
Brox, T., Cremers, D.: On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional. IJCV 84(2), 184–193 (2009)
Chellappa, R., Chatterjee, S.: Classification of textures using Gaussian Markov random fields. IEEE Trans. ASSP 33(4), 959–963 (1985)
Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2), 195–215 (2007)
Derin, H., Cole, W.: Segmentation of textured images using Gibbs random fields. Computer Vision, Graphics, and Image Processing, pp. 72–98 (1986)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59(2), 167–181 (2004)
Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. PAMI 31(12), 2290–2297 (2009)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. ICCV 2001, vol. 2, pp. 416–423 (2001)
Mester, R., Franke, U.: Statistical model based image segmentation using region growing, contour relaxation and classification. In: Proc. SPIE Visual Communications and Image Processing, Cambridge, MA, pp. 616–624 (1988)
Ren, X.R., Malik, J.: Learning a classification model for segmentation. In: Proc. ICCV, vol. 1, pp. 10–17. IEEE Computer Society Press, Los Alamitos (2003)
Rother, C.: Tutorial on map inference in discrete models. In: ICCV (2009)
Sclove, S.L.: Application of the conditional population-mixture model to image segmentation. IEEE Trans. PAMI 5, 428–433 (1983)
Therrien, C.W.: An estimation-theoretic approach to terrain image segmentation. Computer Vision, Graphics, and Image Processing 22(3), 313–326 (1983)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. PAMI, 929–944 (2007)
Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010)
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Mester, R., Conrad, C., Guevara, A. (2011). Multichannel Segmentation Using Contour Relaxation: Fast Super-Pixels and Temporal Propagation. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_24
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DOI: https://doi.org/10.1007/978-3-642-21227-7_24
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