Abstract
Hair is a very important part of human appearance. Robust and accurate hair segmentation is difficult because of challenging variation of hair color and shape. In this paper, we propose a novel Compositional Exemplar-based Model (CEM) for hair style segmentation. CEM generates an adaptive hair style (a probabilistic mask) for the input image automatically in the manner of Divide-and-Conquer, which can be divided into decomposition stage and composition stage naturally. For the decomposition stage, we learn a strong ranker based on a group of weak similarity functions emphasizing the Semantic Layout similarity (SLS) effectively; in the composition stage, we introduce the Neighbor Label Consistency (NLC) Constraint to reduce the ambiguity between data representation and semantic meaning and then recompose the hair style using alpha-expansion algorithm. Final segmentation result is obtained by Dual-Level Conditional Random Fields. Experiment results on face images from Labeled Faces in the Wild data set show its effectiveness.
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
Paris, S., Briceo, H.M., Sillion, F.X.: Capture of hair geometry from multiple images. In: SIGGRAPH, Los Angeles, CA, United states, vol. 23, pp. 712–719 (2004)
Paris, S., Chang, W., Kozhushnyan, O.I., Jarosz, W., Matusik, W., Zwicker, M., Durand, F.: Hair photobooth: Geometric and photometric acquisition of real hairstyles. In: SIGGRAPH, vol. 27 (2008)
Ward, K., Bertails, F., Kim, T.Y., Marschner, S.R., Cani, M.P., Lin, M.C.: A survey on hair modeling: Styling, simulation, and rendering. IEEE Transactions on Visualization and Computer Graphics 13, 213–233 (2007)
Yacoob, Y., Davis, L.S.: Detection and analysis of hair. PAMI 28, 1164–1169 (2006)
chih Lee, K., Anguelov, D., Sumengen, B., Gokturk, S.B.: Markov random field models for hair and face segmentation. In: AFG, Amsterdam, pp. 1–6 (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)
Borenstein, E., Ullman, S.: Combined top-down/bottom-up segmentation. PAMI 30, 2109–2125 (2007)
Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. PAMI 31, 1955–1967 (2009)
Jojic, N., Perina, A., Cristani, M., Murino, V., Frey, B.: Stel component analysis: Modeling spatial correlations in image class structure. In: CVPR (2009)
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? PAMI 26, 147–159 (2004)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. PAMI 23, 1222–1239 (2001)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. PAMI 26, 1124–1137 (2004)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition. In: ICCV (2005)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933–969 (2004)
Kohli, P., Ladick, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. In: CVPR, Anchorage, AK, United states (2008)
Larlus, D., Jurie, F.: Combining appearance models and markov random fields for category level object segmentation. In: CVPR, Anchorage, AK, pp. 1–7 (2008)
Pantofaru, C., Schmid, C., Hebert, M.: Object recognition by integrating multiple image segmentations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 481–494. Springer, Heidelberg (2008)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. PAMI 23, 800–810 (2001)
Huang, G.B., Berg, T., Ramesh, M.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments, University of Massachusetts, Amherst, Technical Report (2007)
Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. PAMI 29, 671–686 (2007)
Zhang, L., Ai, H., Xin, S., Huang, C., Tsukiji, S., Lao, S.: Robust face alignment based on local texture classifiers. In: ICIP, vol. 2, pp. 354–357 (2005)
Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems 20, 422–446 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, N., Ai, H., Lao, S. (2011). A Compositional Exemplar-Based Model for Hair Segmentation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-19318-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19317-0
Online ISBN: 978-3-642-19318-7
eBook Packages: Computer ScienceComputer Science (R0)