Laplacian Sparse Coding of Scenes for Video Classification
Yifang Yin, Zhenguang Liu, Satyam, Roger Zimmermann · 2016
The challenging task of dynamic scene classification in unconstrained videos has drawn much research attention in recent years. Most existing work has focused on extracting local descriptors from spatiotemporal interesting points or subregions, followed by feature aggregation with advanced coding techniques. In this study, we analyse the effectiveness of global image descriptors and propose a novel Laplacian Sparse Coding of Scenes (LSCoS) method for video categorization. Previous methods neglect the semantic relationship among the visual scenes in the dictionary, resulting in generating different representations for videos with similar content. Intuitively, the coefficients assigned to the visual scenes of the same class should be promoted or demoted simultaneously for consistency concerns. To build upon the above ideas, we construct a Laplacian matrix by exploiting the connections between the representative scenes from each class and formulate the objective function with L1 and Laplacian regularizers to generate more robust semantically consistent sparse codes. Comprehensive experiments have been conducted on two public dynamic scene recognition datasets, namely Maryland and YUPENN. Experimental results demonstrate the effectiveness of our proposed approach, as our solution achieves the state-of-the-art classification rates and improves the accuracy by 2.86% ~ 16.93% compared with the existing methods.