Abstract
We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a statistical MAP inference approach to merge textural similarity with information about color and location. Combined with recently developed convex multilabel optimization methods this leads to an efficient algorithm for interactive segmentation, which is easily parallelized on graphics hardware. The provided approach outperforms state-of-the-art interactive segmentation methods on the Graz Benchmark.
This work was supported by the German Academic Exchange Service (DAAD) and the ERC Starting Grant ’Convex Vision’.
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References
Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: IEEE Int. Conf. on Computer Vision (2001)
Brox, T., Cremers, D.: On local region models and a statistical interpretation of the piecewise smooth mumford-shah functional. Int. J. of Computer Vision 84, 184–193 (2009)
Chambolle, A., Cremers, D., Pock, T.: A convex approach for computing minimal partitions. Tech. rep., TR-2008-05, University of Bonn, Germany (2008)
Chan, T., Esedoḡlu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics 66(5), 1632–1648 (2006)
Chen, Y., Ranftl, R., Pock, T.: Insights into analysis operator learning: From patch-based sparse models to higher order MRFs. IEEE Trans. on Image Processing 23, 1060–1072 (2014)
Dice, L.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)
Elad, M., Milanfar, P., Rubinstein, R.: Analysis versus synthesis in signal priors. Inverse Problems 3(3), 947–968 (2007)
Federer, H.: Geometric Measure Theory. Springer (1996)
Grady, L.: Random walks for image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)
Hawe, S., Kleinsteuber, M., Diepold, K.: Analysis Operator Learning and Its Application to Image Reconstruction. IEEE Trans. on Image Processing 22(6), 2138–2150 (2013)
Kiechle, M., Hawe, S., Kleinsteuber, M.: A joint intensity and depth co-sparse analysis model for depth map super-resolution. In: IEEE Int. Conf. on Computer Vision (2013)
Lellmann, J., Kappes, J., Yuan, J., Becker, F., Schnörr, C.: Convex multiclass image labeling by simplex-constrained total variation. Tech. rep., HCI, IWR, University of Heidelberg (2008)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Discriminative learned dictionaries for local image analysis. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Michelot, C.: A finite algorithm for finding the projection of a point onto the canonical simplex of R n. Journal of Optimization Theory and Applications 50(1), 195–200 (1986)
Mignotte, M.: MDS-based segmentation model for the fusion of contour and texture cues in natural images. Computer Vision and Image Understanding (2012)
Mobahi, H., Rao, S., Yang, A., Sastry, S., Ma, Y.: Segmentation of natural images by texture and boundary compression. Int. J. of Computer Vision 95 (2011)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42, 577–685 (1989)
Nam, S., Davies, M.E., Elad, M., Gribonval, R.: The Cosparse Analysis Model and Algorithms. Applied and Computational Harmonic Analysis 34(1), 30–56 (2013)
Nieuwenhuis, C., Cremers, D.: Spatially varying color distributions for interactive multi-label segmentation. IEEE Trans. on Patt. Anal. and Mach. Intell. 35(5), 1234–1247 (2013)
Nieuwenhuis, C., Toeppe, E., Cremers, D.: A survey and comparison of discrete and continuous multi-label optimization approaches for the Potts Model. Int. J. of Computer Vision 104(3), 223–240 (2013)
Parzen, E.: On the estimation of a probability density function and the mode. Annals of Mathematical Statistics (1962)
Pock, T., Cremers, D., Bischof, H., Chambolle, A.: An algorithm for minimizing the piecewise smooth Mumford-Shah functional. In: IEEE Int. Conf. on Computer Vision (2009)
Pock, T., Chambolle, A.: Diagonal preconditioning for first order primal-dual algorithms in convex optimization. In: IEEE Int. Conf. on Computer Vision, pp. 1762–1769 (2011)
Ravishankar, S., Bresler, Y.: Learning Sparsifying Transforms. IEEE Transactions on Signal Processing 61(5), 1072–1086 (2013)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (Proc. SIGGRAPH) 23(3), 309–314 (2004)
Santner, J., Pock, T., Bischof, H.: Interactive multi-label segmentation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 397–410. Springer, Heidelberg (2011)
Santner, J., Unger, M., Pock, T., Leistner, C., Saffari, A., Bischof, H.: Interactive texture segmentation using random forests and total variation. In: British Machine Vision Conference (2009)
Tai, Y., Jia, J., Tang, C.: Soft color segmentation and its applications. IEEE Trans. on Patt. Anal. and Mach. Intell. 29(9), 1520–1537 (2007)
Tran, T.: Combining color and texture for a robust interactive segmentation algorithm. In: IEEE Int. Conf. Comp. and Comm. Techn., Research, Innov. and Vision for the Future (2010)
Unger, M., Pock, T., Cremers, D., Bischof, H.: TVSeg - interactive total variation based image segmentation. In: British Machine Vision Conference (2008)
Xiang, S., Nie, F., Zhang, C.: Texture image segmentation: An interactive framework based on adaptive features and transductive learning. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 216–225. Springer, Heidelberg (2006)
Yaghoobi, M., Nam, S., Gribonval, R., Davies, M.E.: Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling. IEEE Transactions on Signal Processing 61(9), 2341–2355 (2013)
Yang, A., Wright, J., Ma, Y., Sastry, S.: Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding (2008)
Zach, C., Gallup, D., Frahm, J.M., Niethammer, M.: Fast global labeling for real-time stereo using multiple plane sweeps. In: Vision, Modeling and Visualization Workshop (VMV) (2008)
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Nieuwenhuis, C., Hawe, S., Kleinsteuber, M., Cremers, D. (2014). Co-Sparse Textural Similarity for Interactive Segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8694. Springer, Cham. https://doi.org/10.1007/978-3-319-10599-4_19
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