Semi-supervised dictionary learning via local sparse constraints for violence detection

Publisher:
Elsevier
Publication Type:
Journal Article
Citation:
Pattern Recognition Letters, 2018, 107 pp. 98 - 104
Issue Date:
2018-05-01
Full metadata record
Files in This Item:
Filename Description Size
20170713_prletters-violence_Tao.pdfAccepted Manuscript Version1.1 MB
Adobe PDF
© 2017 Elsevier B.V. In this paper, we propose a novel semi-supervised learning framework for violence detection in video surveillance. With this framework, a classifier which distinguishes violent behavior from normal behavior can be trained using inexpensive unlabeled data with the assistance of human operators. Our approach can learn a single dictionary and a predictive linear classifier jointly. Specifically, we integrate the reconstruction error of labeled and unlabeled data, representation constraints and the coefficient incoherence into an objective function for dictionary learning, which enhances the representative and discriminative power of the established dictionary. This has contributed to that the dictionary and the classifier learned from the labeled set yield very small generalization error on unseen data. Experimental results on benchmark datasets have demonstrated the effectiveness of our approach in violence detection.
Please use this identifier to cite or link to this item: