Abstract
In many segmentation scenarios, labeled images contain rich structural information about spatial arrangement and shapes of the objects. Integrating this rich information into supervised learning techniques is promising as it generates models which go beyond learning class association, only. This paper proposes a new supervised forest model for joint classification-regression which exploits both class and structural information. Training our model is achieved by optimizing a joint objective function of pixel classification and shape regression. Shapes are represented implicitly via signed distance maps obtained directly from ground truth label maps. Thus, we can associate each image point not only with its class label, but also with its distances to object boundaries, and this at no additional cost regarding annotations. The regression component acts as spatial regularization learned from data and yields a predictor with both class and spatial consistency. In the challenging context of simultaneous multi-organ segmentation, we demonstrate the potential of our approach through experimental validation on a large dataset of 80 three-dimensional CT scans.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision Forests: A Unified Framework. Foundations and Trends in Computer Graphics and Vision 7(2-3) (2011)
Ho, T.K.: Random Decision Forests. In: ICDAR, vol. 1, pp. 278–282 (1995)
Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. PAMI 20(8), 832–844 (1998)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-Time Human Pose Recognition in Parts from a Single Depth Image. In: CVPR, pp. 1297–1304 (2011)
Amit, Y., Geman, D.: Shape Quantization and Recognition with Randomized Trees. Neural Computation 9, 1545–1588 (1997)
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
Bosch, A., Zisserman, A., Munoz, X.: Image Classification Using Random Forests and Ferns. In: ICCV (2007)
Maree, R., Geurts, P., Piater, J., Wehenkel, L.: Random Subwindows for Robust Image Classification. In: CVPR (2005)
Caruana, R., Karampatziakis, N., Yessenalina, A.: An Empirical Evaluation of Supervised Learning in High Dimensions. In: ICML, pp. 96–103 (2008)
Yin, P., Criminisi, A., Essa, I., Winn, J.: Tree-based Classifiers for Bilayer Video Segmentation. In: CVPR, pp. 1–8 (2007)
Payet, N., Todorovic, S.: (RF)2 Random Forest Random Field. In: NIPS (2010)
Kontschieder, P., Rota Buló, S., Bischof, H., Pelillo, M.: Structured class-labels in random forests for semantic image labelling. In: ICCV (2011)
Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 184–196. Springer, Heidelberg (2011)
Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., Kohli, P.: Decision Tree Fields. In: ICCV (2011)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial Structures for Object Recognition. IJCV 61(1), 55–79 (2005)
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient Regression of General-Activity Human Poses from Depth Images. In: ICCV, pp. 415–422 (2011)
Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.: Hough Forests for Object Detection, Tracking, and Action Recognition. PAMI 33(11), 2188–2202 (2011)
Cootes, T., Edwards, G., Taylor, C.: Active Appearance Models. PAMI 23(6), 681–685 (2001)
Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. IJCV 70(2), 109–131 (2006)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. IJCV 57(2), 137–154 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A. (2012). Joint Classification-Regression Forests for Spatially Structured Multi-object Segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33765-9_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-33765-9_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33764-2
Online ISBN: 978-3-642-33765-9
eBook Packages: Computer ScienceComputer Science (R0)