To construct optimal training set for video annotation

This paper exploits the criteria to optimize the training set construction for video annotation. Most existing learning-based semantic annotation approaches require a large training set to achieve good generalization capacity, in which a considerable amount of labor-intensively manual labeling is desirable. However, it is observed that the generalization capacity of a classifier highly depends on the geometrical distribution rather than the size of the training data. We argue that a training set which includes most temporal and spatial distribution of the whole data will achieve a satisfying performance even in the case of limited size of training set. In order to capture the geometrical distribution characteristics of a given video collection, we propose the following four metrics for constructing an optimal training set, including Salience Time Dispersiveness Spatial Dispersiveness and Diversity. Moreover, based on these metrics, we propose a set of optimization rules to capture the most distribution information of the whole data for a training set with a given size. Experimental results demonstrate that these rules are effective for training set construction for video annotation, and significantly outperform random training set selection as well.