Semisupervised feature selection via spline regression for video semantic recognition

Han, Yahong, Yang, Yi, Yan, Yan, Ma, Zhigang, Sebe, Nicu and Zhou, Xiaofang (2015) Semisupervised feature selection via spline regression for video semantic recognition. IEEE Transactions on Neural Networks and Learning Systems, 26 2: 252-264. doi:10.1109/TNNLS.2014.2314123

Author Han, Yahong
Yang, Yi
Yan, Yan
Ma, Zhigang
Sebe, Nicu
Zhou, Xiaofang
Title Semisupervised feature selection via spline regression for video semantic recognition
Journal name IEEE Transactions on Neural Networks and Learning Systems   Check publisher's open access policy
ISSN 2162-2388
Publication date 2015-02-01
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TNNLS.2014.2314123
Open Access Status
Volume 26
Issue 2
Start page 252
End page 264
Total pages 13
Place of publication Piscataway NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2015
Language eng
Formatted abstract
To improve both the efficiency and accuracy of video semantic recognition, we can perform feature selection on the extracted video features to select a subset of features from the high-dimensional feature set for a compact and accurate video data representation. Provided the number of labeled videos is small, supervised feature selection could fail to identify the relevant features that are discriminative to target classes. In many applications, abundant unlabeled videos are easily accessible. This motivates us to develop semisupervised feature selection algorithms to better identify the relevant video features, which are discriminative to target classes by effectively exploiting the information underlying the huge amount of unlabeled video data. In this paper, we propose a framework of video semantic recognition by semisupervised feature selection via spline regression (S2FS2R). Two scatter matrices are combined to capture both the discriminative information and the local geometry structure of labeled and unlabeled training videos: A within-class scatter matrix encoding discriminative information of labeled training videos and a spline scatter output from a local spline regression encoding data distribution. An ℓ2,1-norm is imposed as a regularization term on the transformation matrix to ensure it is sparse in rows, making it particularly suitable for feature selection. To efficiently solve S2FS2R, we develop an iterative algorithm and prove its convergency. In the experiments, three typical tasks of video semantic recognition, such as video concept detection, video classification, and human action recognition, are used to demonstrate that the proposed S2FS2R achieves better performance compared with the state-of-the-art methods.
Keyword ℓ2,1
Semisupervised feature selection
Spline regression
Video analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 10 Apr 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 23 times in Scopus Article | Citations
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