Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition

• Published in: IEEE transaction on neural networks and learning systems 2015-02, Vol.26 (2), p.252-264
• Main Authors: Yahong Han, Yi Yang, Yan Yan, Zhigang Ma, Sebe, Nicu, Xiaofang Zhou
• Format: Article
• Language: English
• Subjects: Drafting; Encoding; Feature extraction; Feature recognition; Geometry; Recognition; Regression; Scatter; Semantics; semisupervised feature selection; Sparse matrices; spline regression; Splines; Splines (mathematics); Training; Vectors; video analysis; Video data
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2014.2314123

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 (S 2 FS 2 R). 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 ℓ 2,1 -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 S 2 FS 2 R, 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 S 2 FS 2 R achieves better performance compared with the state-of-the-art methods.