Abstract
In this paper, we propose a novel appearance-based representation, called Structured Ordinal Feature (SOF). SOF is a binary string encoded by combining eight ordinal blocks in a circle symmetrically. SOF is invariant to linear transformations on images and is flexible enough to represent different local structures of different complexity. We further extend SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position. In this way, MSOF encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation. We also present an efficient algorithm for computing MSOF using integral images. Based on MSOF, statistical analysis and learning are performed to select most effective features and construct classifiers. The proposed method is evaluated with face recognition experiments, in which we achieve a high rank-1 recognition rate of 98.24% on FERET database.
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
Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 469–481. Springer, Heidelberg (2004)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Crow, F.: Summed-area tables for texture mapping. In: SIGGRAPH, vol. 18(3), pp. 207–212 (1984)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Sequoia Hall, Stanford Univerity (July 1998)
Lades, M., Vorbruggen, J., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 42, 300–311 (1993)
Liao, S., Lei, Z., Zhu, X., Sun, Z., Li, S., Tan, T.: Face recognition using ordinal features. In: Zhang, D., Jain, A.K. (eds.) Advances in Biometrics. LNCS, vol. 3832, pp. 40–46. Springer, Heidelberg (2005)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)
Moghaddam, B., Nastar, C., Pentland, A.: A Bayesain Similarity measure for direct image matching. Media Lab Tech. Report No.393, MIT (August 1996)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)
Penev, P., Atick, J.: Local feature analysis: A general statistical theory for object representation. Neural Systems 7(3), 477–500 (1996)
Sadr, J., Mukherjee, S., Thoresz, K., Sinha, P.: Toward the fidelity of local ordinal encoding. In: Proceedings of the Fifteenth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada (December 3-8, 2001)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91 (1998)
Schneiderman, H.: Toward feature-centric evaluation for efficient cascaded object detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 1007–1013 (June 27 - July 2, 2004)
Simard, P.Y., Bottou, L., Haffner, P., Cun, Y.L.: Boxlets: a fast convolution algorithm for signal processing and neural networks. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 571–577. MIT Press, Cambridge (1998)
Sinha, P.: Toward qualitative representations for recognition. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 249–262. Springer, Heidelberg (2002)
Thoresz, K.J.: On qualitative representations for recognition. Master’s thesis, MIT (July 2002)
Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Viola, P., Jones, M.: Robust real time object detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada (July 13, 2001)
Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.v.d.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liao, S., Lei, Z., Li, S.Z., Yuan, X., He, R. (2007). Structured Ordinal Features for Appearance-Based Object Representation. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol 4778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75690-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-75690-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75689-7
Online ISBN: 978-3-540-75690-3
eBook Packages: Computer ScienceComputer Science (R0)