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. This paper argues 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.
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