Abstract
In boosting-based object detectors, weak classifiers are often build on Haar-like features using conventional integral images. That approach leads to the utilization of simple rectangle-shaped structures which are only partial suitable for curved-shaped structures, as present in natural object classes such as faces. In this paper, we propose a new class of fractal features based on space-filling curves, a special type of fractals also known as Peano curves. Our method incorporates the new feature class by computing integral images along these curves. Therefore space-filling curves offer our proposed features to describe a wider variety of shapes including self-similar structures. By introducing two subtypes of fractal features, three-point and four-point features, we get a richer representation of curved and topology separated but correlated structures. We compare AdaBoost using conventional Haar-like features and our proposed fractal feature class in several experiments on the well-known MIT+CMU upright face test set and a microscopy cell test set.
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References
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Sung, K.K., Poggio, T., Rowley, H.A., Baluja, S., Kanade, T.: MIT+CMU frontal face dataset a, b and c. (MIT+CMU)
Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Technical report, Tech. rep., Microsoft Research (2010)
Li, S.Z., Zhang, Z., Shum, H.Y., Zhang, H.: Floatboost learning for classification. Advances in Neural Information Processing Systems, Microsoft Research Asia (2002)
Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. Advances in Neural Information Processing 18, 1417–1426 (2007)
Warmuth, M.K., Glocer, K.A., Vishwanathan, S.V.N.: Entropy regularized lPBoost. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds.) ALT 2008. LNCS (LNAI), vol. 5254, pp. 256–271. Springer, Heidelberg (2008)
Warmuth, M.K., Glocer, K., Raetsch, G.: Boosting algorithms for maximizing the soft margin. Advances in Neural Information Processing Systems 20, 1585–1592 (2008)
Jiang, J.L., Loe, K.F.: S-adaboost and pattern detection in complex environment. In: CVPR 2003, pp. 413–418.
Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: ICCV 2007 (2007)
Wang, X., Han, T.X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39. IEEE (2009)
Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007)
Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: IEEE ICIP (2002)
Pham, M.T., Gao, Y., Hoang, V.D.D., Cham, T.J.: Fast polygonal integration and its application in extending haar-like features to improve object detection. In: CVPR 2010 (2010)
Freund, Y., Schapire, R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14, 771–780 (1999)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference, pp. 148–156.
Barnsley, M.F., Rising, H.: Fractals Everywhere. Academic Press Professional, Boston (1993)
Ansari, A.C., Fineberg, A.: Image data ordering and compression using peano scan and lot. Consumer Electronics, 1992. Digest of Technical Papers. ICCE (1992)
Dafner, R., Cohen-Or, D., Matias, Y.: Context-based space filling curves. Comput. Graph. Forum 19 (2000)
Lam, W.M., Shapiro, J.M., Sarnoff, D.: A class of fast algorithms for the peano-hilbert space-filling curve. In: ICIP(1), pp. 638–641 (1994)
Phuvan, S., Oh, T.K., Caviris, N.P., Li, Y., Szu, H.H.: Texture analysis by space-filling curves and one-dimensional haar wavelets. Optical Engineering 31(09), 1899–1906 (1992)
Lindenmayer, A.: Mathematical models for cellular interaction in development: Parts i and ii. Journal of Theoretical Biology 18, 280–315 (1968)
BioID-AG: Bioid face database. BioID Switzerland (2010)
Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database. Pattern Recognition Association of South Africa (2010), http://www.milbo.org/muct
Bradski, G.: The opencv library. Dr. Dobb’s Journal of Software Tools (2000)
Crowther, P.S., Cox, R.J.: A method for optimal division of data sets for use in neural networks. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 1–7. Springer, Heidelberg (2005)
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Ehlers, A., Baumann, F., Rosenhahn, B. (2014). Boosted Fractal Integral Paths for Object Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_44
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DOI: https://doi.org/10.1007/978-3-319-14364-4_44
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