Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos

Abstract

We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos. We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification tasks where many correlated observed variables are necessary to make a decision. We then compare several structure learning objective functions, which aim at finding out the structure that yields the best classification results, extending existing solutions in the literature. Experimental results on a comprehensive data set of 7 videos show that a discriminative objective function based on conditional likelihood yields the best results, while augmented approaches offer a good compromise between learning speed and classification accuracy.

Event-based media processing and analysis: A survey of the literature

“…Bayesian Networks were used to define a probability distribution over the features. The structure of the BNs is a sensitive issue, but such a structure can be efficiently learned from the data [77]. Moreover, the huge advantage of BNs over the popular SVMs is to have a very low parameter learning cost, and no hyperparameters to tune, this yielding better generalization capabilities.…”

Audio event detection in movies using multiple audio words and contextual Bayesian networks

“…Bayesian networks are used to define a probability distribution over the features. The structure of the BNs is a sensitive issue, but such a structure can be efficiently learned from the data [13]. Moreover, the huge advantage of BNs over the popular SVMs is to have a very low parameter learning cost, and no hyperparameters to tune, this yielding better generalization capabilities.…”