Abstract
A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position. We show this leads to fast and accurate matching when combined with a statistical shape model. We evaluate the technique in detail, and compare with a range of commonly used alternatives on several different datasets. We show that the random forest regression method is significantly faster and more accurate than equivalent discriminative, or boosted regression based methods trained on the same data.
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
Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Milborrow, S., Nicolls, F.: Locating Facial Features with an Extended Active Shape Model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 504–513. Springer, Heidelberg (2008), http://www.milbo.users.sonic.net/stasm
Felzenszwalb, P., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61, 55–79 (2005)
Cristinacce, D., Cootes, T.F.: Automatic feature localisation with constrained local models. Pattern Recognition 41, 3054–3067 (2008)
Saragih, J.M., Lucey, S., Cohn, J.F.: Deformable model fitting by regularized mean-shifts. International Journal of Computer Vision 200-215 (2011)
Gall, J., Lempitsky, V.: Class-specic hough forests for object detection. In: CVPR (2009)
Valstar, M., Martinez, B., Binefa, X., Pantic, M.: Facial point detection using boosted regression and graph models. In: CVPR (2010)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 681–685 (2001)
Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: CVPR (2011)
Covell, M.: Eigen-points: Control-point location using principal component analysis. In: International Conference on Automatic Face and Gesture Recognition, Killington, USA, pp. 122–127 (1996)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Saragih, J., Goecke, R.: A non-linear discriminative approach to AAM fitting. In: Proc. ICCV (2007)
Tresadern, P., Sauer, P., Cootes, T.: Additive update predictors in active appearance models. In: British Machine Vision Conference. BMVA Press (2010)
Sauer, P., Cootes, T., Taylor, C.: Accurate regression procedures for active appearance models. In: BMVC (2011)
Zhou, S., Comaniciu, D.: Shape regression machine. In: Information Processing in Medical Imaging, pp. 13–25. IEEE Computer Society Press (2007)
Zimmermann, K., Matas, J., Svoboda, T.: Tracking by an optimal sequence of linear predictors. IEEE Trans. PAMI 30, 677–692 (2009)
Ong, E., Bowden, R.: Robust facial feature tracking using shape-constrained multiresolution-selected linear predictors. IEEE PAMI 33, 1844–1859 (2004)
Dollár, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR, pp. 1078–1085 (2010)
Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)
Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV Workshop on Statistical Learning in Computer Vision (2004)
Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3d human pose annotations. In: ICCV (2009)
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: ICCV (2011)
Dantone, M., Gall, J., Fanelli, G., Van Gool, L.: Real-time Facial Feature Detection using Conditional Regression Forests. In: CVPR (2012)
Luo, P., Wang, X., Tang, X.: Hierarchical Face Parsing vis Deep Learning. In: CVPR (2012)
Cao, X., Wei, Y., Wen, F., Sun, J.: Face Alignment by Explicit Shape Regression. In: CVPR (2012)
Criminisi, A., Shotton, J., Robertson, D.: Konukoglu, E.: Regression forests for efficient anatomy detection and localisation in CT studies. In: Medical Computer Vision Workshop, pp. 106–117 (2010)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511–518 (2001)
Schulter, S., Leistner, C., Roth, P.M., Gool, L.V., Bischof, H.: On-line hough forests. In: BMVC (2011)
Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSdb: The extended m2vts database. In: Proc. 2nd Conf. on Audio and Video-Based Biometric Personal Verification, pp. 72–77. Springer (1999)
Kostinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In: ICCV Workshops, pp. 2144–2151. IEEE (2011)
Lindner, C., Thiagarajah, S., Wilkinson, J., arcOGEN Consortium, Wallis, G., Cootes, T.F.: Accurate fully automatic femur segmentation in pelvic radiographs using regression voting. In: MICCAI (2012)
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
Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P. (2012). Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33786-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-33786-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33785-7
Online ISBN: 978-3-642-33786-4
eBook Packages: Computer ScienceComputer Science (R0)