DocumentCode :
1482560
Title :
{\\rm S}^{3}{\\rm MKL} : Scalable Semi-Supervised Multiple Kernel Learning for Real-World Image Applications
Author :
Wang, Shuhui ; Huang, Qingming ; Jiang, Shuqiang ; Tian, Qi
Author_Institution :
Key Lab. of Intell. Inf. Process., Inst. of Comput. Technol., Beijing, China
Volume :
14
Issue :
4
fYear :
2012
Firstpage :
1259
Lastpage :
1274
Abstract :
We study the visual learning models that could work efficiently with little ground-truth annotation and a mass of noisy unlabeled data for large scale Web image applications, following the subroutine of semi-supervised learning (SSL) that has been deeply investigated in various visual classification tasks. However, most previous SSL approaches are not able to incorporate multiple descriptions for enhancing the model capacity. Furthermore, sample selection on unlabeled data was not advocated in previous studies, which may lead to unpredictable risk brought by real-world noisy data corpse. We propose a learning strategy for solving these two problems. As a core contribution, we propose a scalable semi-supervised multiple kernel learning method (S3MKL) to deal with the first problem. The aim is to minimize an overall objective function composed of log-likelihood empirical loss, conditional expectation consensus (CEC) on the unlabeled data and group LASSO regularization on model coefficients. We further adapt CEC into a group-wise formulation so as to better deal with the intrinsic visual property of real-world images. We propose a fast block coordinate gradient descent method with several acceleration techniques for model solution. Compared with previous approaches, our model better makes use of large scale unlabeled images with multiple feature representation with lower time complexity. Moreover, to address the issue of reducing the risk of using unlabeled data, we design a multiple kernel hashing scheme to identify the “informative” and “compact” unlabeled training data subset. Comprehensive experiments are conducted and the results show that the proposed learning framework provides promising power for real-world image applications, such as image categorization and personalized Web image re-ranking with very little user interaction.
Keywords :
Internet; computational complexity; gradient methods; image processing; learning (artificial intelligence); CEC; S3MKL; SSL approach; conditional expectation consensus; fast block coordinate gradient descent method; ground-truth annotation; group LASSO regularization; group-wise formulation; image categorization; intrinsic visual property; large scale Web image applications; large scale unlabeled images; learning strategy; log-likelihood empirical loss; overall objective function; personalized Web image re-ranking; real-world image applications; scalable semi-supervised multiple kernel learning; time complexity; visual learning models; Data models; Kernel; Learning systems; Noise measurement; Training; Training data; Visualization; Image categorization; multiple kernel learning; multiple kernel locality sensitive hashing; personalized image re-ranking; semi-supervised learning;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2012.2193120
Filename :
6177671
Link To Document :
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