Abstract
We present a new approach for contextual semantic segmentation and introduce a new tree-based framework, which combines local information and context knowledge in a single model. The method itself is also suitable for anytime classification scenarios, where the challenge is to estimate a label for each pixel in an image while allowing an interruption of the estimation at any time. This offers the application of the introduced method in time-critical tasks, like automotive applications, with limited computational resources unknown in advance. Label estimation is done in an iterative manner and includes spatial context right from the beginning. Our approach is evaluated in extensive experiments showing its state-of-the-art performance on challenging street scene datasets with anytime classification abilities.
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References
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Csurka, G., Perronnin, F.: An efficient approach to semantic segmentation. IJCV 95(2), 198–212 (2011)
Dahlkamp, H., Kaehler, A., Stavens, D., Thrun, S., Bradski, G.: Self-supervised monocular road detection in desert terrain. In: Robotics: Science and Systems (2006)
DeCoste, D.: Anytime interval-valued outputs for kernel machines: Fast support vector machine classification via distance geometry. In: Proceedings of the International Conference on Machine Learning (ICML 2002), pp. 99–106 (2002)
Esmeir, S., Markovitch, S.: Anytime learning of anycost classifiers. Machine Learning 82(3), 445–473 (2011)
Fink, M., Perona, P.: Mutual boosting for contextual inference. In: Advances in Neural Information Processing Systems (NIPS 2003), vol. 16, pp. 1515–1522 (2003)
Fröhlich, B., Rodner, E., Denzler, J.: A fast approach for pixelwise labeling of facade images. In: ICPR, pp. 3029–3032 (2010)
Hui, B., Yang, Y., Webb, G.: Anytime classification for a pool of instances. Machine Learning 77(1), 61–102 (2009)
Korč, F., Förstner, W.: eTRIMS image database for interpreting images of man-made scenes. Tech. Rep. TR-IGG-P-2009-01, University of Bonn (2009)
Ladický, Ľ., Sturgess, P., Russell, C., Sengupta, S., Bastanlar, Y., Clocksin, W., Torr, P.: Joint optimisation for object class segmentation and dense stereo reconstruction. In: BMVC, pp. 104.1–104.11 (2010)
Rusu, R.B., Holzbach, A., Bradski, G., Beetz, M.: Detecting and segmenting objects for mobile manipulation. In: Proceedings of IEEE Workshop on Search in 3D and Video (S3DV), pp. 47–54 (2009)
Seidl, T., Assent, I., Kranen, P., Krieger, R., Herrmann, J.: Indexing density models for incremental learning and anytime classification on data streams. In: Proceedings of the 12th Int. Conference on Extending Database Technology, pp. 311–322 (2009)
Shieh, J., Keogh, E.J.: Polishing the right apple: Anytime classification also benefits data streams with constant arrival times. In: Proceedings of the International Conference on Data Mining, pp. 461–470 (2010)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR, pp. 1–8 (2008)
Viola, P., Jones, M.: Robust real-time object detection. IJCV 57, 137–154 (2002)
Yang, M.Y., Förstner, W.: A hierarchical conditional random field model for labeling and classifying images of man-made scenes. In: Proceedings of the IEEE Computer Vision Workshops (ICCV Workshops), pp. 196–203 (2011)
Yang, M.Y., Förstner, W.: Regionwise Classification of Building Facade Images. In: Stilla, U., Rottensteiner, F., Mayer, H., Jutzi, B., Butenuth, M. (eds.) PIA 2011. LNCS, vol. 6952, pp. 209–220. Springer, Heidelberg (2011)
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Fröhlich, B., Rodner, E., Denzler, J. (2012). As Time Goes by—Anytime Semantic Segmentation with Iterative Context Forests. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_1
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DOI: https://doi.org/10.1007/978-3-642-32717-9_1
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