Abstract
The techniques for image analysis and classification generally consider the image sample labels fixed and without uncertainties. The rank regression problem studied in this paper is based on the training samples with uncertain labels, which often is the case for the manual estimated image labels. A core ranking model is designed first as the bilinear fusing of multiple candidate kernels. Then, the parameters for feature selection and kernel selection are learned simultaneously by maximum a posteriori for given samples and uncertain labels. The provable convergency Expectation Maximization (EM) method is used for inferring these parameters in an iterative manner. The effectiveness of the proposed algorithm is finally validated by the extensive experiments on age ranking task and human tracking task. The popular FG-NET and the large scale Yamaha aging database are used for the age estimation experiments, and our algorithm outperforms those state-of-the-art algorithms ever reported by other interrelated literatures significantly. The experiment result of human tracking task also validates its advantage over conventional linear regression algorithm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Hinton G, Rumelhart D, Williams R. Learning representations by back-propagating errors. Nature, 1986, 323(9), 533–536
Hayashi J, Yasumoto M, Ito H, et al. A method for estimating and modeling age and gender using facial image processing. In: Proceedings of the 7th International Conference on Virtual Systems and Multimedia. Berkeley: University of California, 2001, 439–448
Kwon Y, Lobo N. Age classification from facial images. Computer Vision and Image Understanding, 1999, 74(1): 1–21
Lanitis A, Draganova C, Christodoulou C. Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man and Cybernetics (B), 2004, 34(1): 621–628
Geng X, Zhou Z, Zhng Y, et al. Learning from facial aging patterns for automatic age estimation. In: Proceedings of the 14th ACM International Conference on Multimedia (MM’ 06). Santa Barbara: CA, 2006, 307–316
Rerkrai K, Fillbrandt H. Tracking persons under partial scene occlusion using linear regression. In: The 8th International Student Conference on Electrical Engineering. Prague, 2004
Müller K, Mika S, Rätsch G, et al. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181–202
Bilmes J. A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical report, International Computer Science Institute, Berkeley, 1998
The FG-NET aging database. http://sting.cycollege.ac.cy/:_alanitis/fgnetaging/index.htm
Lanitis A, Taylor C, Cootes T. Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 442–455
Edwards G, Lanitis A. Statistical face models: improving specificity. Image and Vision Computing, 1998, 16(3): 203–211
Cootes T, Edwards G, Taylor C. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 681–685
Author information
Authors and Affiliations
Corresponding author
Additional information
A short version of this paper appeared in ICME07.
Rights and permissions
About this article
Cite this article
Yan, S., Wang, H., Liu, J. et al. Ranking with uncertain labels and its applications. Front. Comput. Sc. China 1, 407–412 (2007). https://doi.org/10.1007/s11704-007-0039-7
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s11704-007-0039-7