Abstract
In this work we propose a boosting-based approach to boundary detection that advances the current state-of-the-art. To achieve this we introduce the following novel ideas: (a) we use a training criterion that approximates the F-measure of the classifier, instead of the exponential loss that is commonly used in boosting. We optimize this criterion using Anyboost. (b) We deal with the ambiguous information about orientation of the boundary in the annotation by treating it as a hidden variable, and train our classifier using Multiple-Instance Learning. (c) We adapt the Filterboost approach of [1] to leverage information from the whole training set to train our classifier, instead of using a fixed subset of points. (d) We extract discriminative features from appearance descriptors that are computed densely over the image. We demonstrate the performance of our approach on the Berkeley Segmentation Benchmark.
Chapter PDF
Similar content being viewed by others
References
Bradley, J.K., Schapire, R.E.: Filterboost: Regression and classification on large datasets. In: NIPS (2007)
Marr, D.: Vision. W.H. Freeman, New York (1982)
Konishi, S., Yuille, A.L., Coughlan, J.M., Zhu, S.C.: Statistical Edge Detection: Learning and Evaluating Edge Cues. PAMI 25 (2003)
Martin, D., Fowlkes, C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. PAMI 26, 530–549 (2004)
Ren, X., Fowlkes, C., Malik, J.: Scale-invariant contour completion using crfs. In: ICCV (2005)
Dollar, P., Tu, Z., Belongie, S.: Supervised Learning of Edges and Object Boundaries. In: CVPR (2006)
Arbelaez, P.: Boundary Extraction in Natural Images Using Ultrametric Contour Maps. In: WPOCV (2006)
Maire, M., Arbelaez, P., Fowlkes, C., Malik., J.: Using Contours to Detect and Localize Junctions in Natural Images. In: CVPR (2008)
Ren, X.: Multiscale helps boundary detection. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 533–545. Springer, Heidelberg (2008)
Mairal, J., Leordeanu, M., Bach, F., Hebert, M., Ponce, J.: Discriminative sparse image models for class-specific edge detection and image interpretation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 43–56. Springer, Heidelberg (2008)
Mason, L., Baxter, J., Bartlett, P., Frean, M.: Boosting algorithms as gradient descent. In: NIPS (2000)
Viola, P., Platt, J.C., Zhang, C.: Multiple Instance Boosting and Object Detection. In: NIPS (2006)
Tola, E., Lepetit, V., Fua, P.: A fast local descriptor for dense matching. In: CVPR (2008)
Canny, J.: A Computational Approach to Edge Detection. PAMI 8, 679–698 (1986)
Perona, P., Malik, J.: Detecting and Localizing Edges Composed of Steps, Peaks and Roofs. In: ICCV, pp. 52–57 (1990)
Catanzaro, B., Sundaram, N., Su, B., Lee, Y., Murphy, M., Keutzer, K.: Efficient high-quality image contour detection. In: ICCV (2009)
Arbelaez, P., Maire, M., Fowlkes, C., Malik., J.: From contours to regions: An empirical evaluation. In: CVPR (2009)
Freund, Y., Schapire, R.: Experiments with a new Boosting Algorithm. In: ICML (1996)
Collins, M., Schapire, R.E., Singer, Y.: Logistic regression, adaboost and bregman distances. In: Machine Learning (2000)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. (2000)
Joachims, T.: A support vector method for multivariate performance measures. In: ICML (2005)
Jansche, M.: Maximum expected f-measure training of logistic regression models. In: HLT 2005 (2005)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. (2002)
Kokkinos, I.: Highly accurate boundary detection and grouping. In: CVPR (2010)
Ren, X., Fowlkes, C.C., Malik, J.: Figure/ground assignment in natural images. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 614–627. Springer, Heidelberg (2006)
Kokkinos, I., Yuille, A.: Scale Invariance without Scale Selection. In: CVPR (2008)
Cook, D., Lee, H.: Dimension reduction in binary response regression. JASA 94 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kokkinos, I. (2010). Boundary Detection Using F-Measure-, Filter- and Feature- (F3) Boost. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15552-9_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-15552-9_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15551-2
Online ISBN: 978-3-642-15552-9
eBook Packages: Computer ScienceComputer Science (R0)