Abstract
In this paper, we present a new combined approach for feature extraction, classification, and context modeling in an iterative framework based on random decision trees and a huge amount of features. A major focus of this paper is to integrate different kinds of feature types like color, geometric context, and auto context features in a joint, flexible and fast manner. Furthermore, we perform an in-depth analysis of multiple feature extraction methods and different feature types. Extensive experiments are performed on challenging facade recognition datasets, where we show that our approach significantly outperforms previous approaches with a performance gain of more than 15% on the most difficult dataset.
Sponsored by the Graduate School on Image Processing and Image Interpretation, TMBWK ProExzellenz program.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arbelaez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., Malik, J.: Semantic segmentation using regions and parts. In: CVPR (2012)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. PAMI 24(5), 603–619 (2002)
Csurka, G., Perronnin, F.: An efficient approach to semantic segmentation. IJCV 95(2), 198–212 (2011)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59(2), 167–181 (2004)
Fink, M., Perona, P.: Mutual boosting for contextual inference. In: NIPS, 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)
Fröhlich, B., Rodner, E., Denzler, J.: As Time Goes by—Anytime Semantic Segmentation with Iterative Context Forests. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds.) DAGM/OAGM 2012. LNCS, vol. 7476, pp. 1–10. Springer, Heidelberg (2012)
Gool, L.J.V., Zeng, G., den Borre, F.V., Müller, P.: Towards mass-produced building models. In: Photogrammetric Image Analysis, pp. 209–220 (2007)
Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: ICCV, vol. 1, pp. 654–661. IEEE (October 2005)
Kohli, P., Ladicky, L., Torr, P.: Robust higher order potentials for enforcing label consistency. In: CVPR, pp. 1–8 (2008)
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., Russell, C., Kohli, P., Torr, P.H.S.: Associative hierarchical crfs for object class image segmentation. In: ICCV, pp. 739–746 (2009)
Martinović, A., Mathias, M., Weissenberg, J., Van Gool, L.: A Three-Layered Approach to Facade Parsing. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 416–429. Springer, Heidelberg (2012)
Ripperda, N., Brenner, C.: Evaluation of Structure Recognition Using Labelled Facade Images. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 532–541. Springer, Heidelberg (2009)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR, pp. 1–8 (2008)
Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)
Teboul, O., Simon, L., Koutsourakis, P., Paragios, N.: Segmentation of building facades using procedural shape priors. In: CVPR, pp. 3105–3112 (2010)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3d brain image segmentation. PAMI 32(10), 1744–1757 (2010)
Viola, P., Jones, M.: Robust real-time object detection. IJCV 57, 137–154 (2002)
van de Weijer, J., Schmid, C.: Applying color names to image description. In: ICIP, vol. 3, pp. 493–496 (2007)
Yang, M.Y., Förstner, W.: A hierarchical conditional random field model for labeling and classifying images of man-made scenes. In: 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)
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
Fröhlich, B., Rodner, E., Denzler, J. (2013). Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-37331-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37330-5
Online ISBN: 978-3-642-37331-2
eBook Packages: Computer ScienceComputer Science (R0)