Optimizing Training Set Construction for Video Semantic Classification. (9th December 2007)

• Record Type: Journal Article
• Authors: Tang Tang, Jinhui Jinhui; Hua Hua, Xian-Sheng Xian-Sheng; Song Song, Yan Yan; Mei Mei, Tao Tao; Wu Wu, Xiuqing Xiuqing
• Other Names: Kahrs Kahrs Mark Mark Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2007-12-09
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/693731
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : We exploit the criteria to optimize training set construction for the large-scale video semantic classification. Due to the large gap between low-level features and higher-level semantics, as well as the high diversity of video data, it is difficult to represent the prototypes of semantic concepts by a training set of limited size. In video semantic classification, most of the learning-based approaches require a large training set to achieve good generalization capacity, in which large amounts of labor-intensive manual labeling are ineluctable. However, it is observed that the generalization capacity of a classifier highly depends on the geometrical distribution of the training data rather than the size. We argue that a training set which includes most temporal and spatial distribution information of the whole data will achieve a good performance even if the size of training set is limited. In order to capture the geometrical distribution characteristics of a given video collection, we propose four metrics for constructing/selecting an optimal training set, including salience, temporal dispersiveness, spatial dispersiveness, and diversity. Furthermore, based on these metrics, we propose a set of optimization rules to capture the most distribution information of the whole data using a training set with a given size. Experimental results demonstrate these rules are effective for training set construction in video semantic classification, and significantly outperform random training set selection.