- Research Article
- Open Access
Optimizing Training Set Construction for Video Semantic Classification
EURASIP Journal on Advances in Signal Processing volume 2008, Article number: 693731 (2007)
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.
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Tang, J., Hua, XS., Song, Y. et al. Optimizing Training Set Construction for Video Semantic Classification. EURASIP J. Adv. Signal Process. 2008, 693731 (2007). https://doi.org/10.1155/2008/693731
- Geometrical Distribution
- Semantic Concept
- Distribution Information
- Optimization Rule
- Generalization Capacity