tailieunhanh - Báo cáo hóa học: "Research Article Optimizing Training Set Construction for Video Semantic Classification"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Optimizing Training Set Construction for Video Semantic Classification | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 693731 10 pages doi 2008 693731 Research Article Optimizing Training Set Construction for Video Semantic Classification Jinhui Tang 1 Xian-Sheng Hua 2 Yan Song 1 Tao Mei 2 and Xiuqing Wu1 1 Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei 230027 China 2 Microsoft Research Asia Beijing 100080 China Correspondence should be addressed to Jinhui Tang jhtang@ Received 9 March 2007 Revised 14 September 2007 Accepted 12 November 2007 Recommended by Mark Kahrs 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 .