Sports video segmentation using a hierarchical hidden CRF

Abstract

Hidden Markov Models (HMMs) are very popular generative models for sequence data. Recent research has, however, shown that Conditional Random Fields (CRFs), a type of discriminative model, outperform HMMs in many tasks. We have previously proposed Hierarchical Hidden Conditional Random Fields (HHCRFs), a discriminative model corresponding to hierarchical HMMs (HHMMs). Given observations, HHCRFs model the conditional probability of the states at the upper levels. States at the lower levels are hidden and marginalized in the model definition. In addition, we have developed a parameter learning algorithm that requires only the states at the upper levels in the training data. Previously we applied HHCRFs to the segmentation of electroencephalographic (EEG) data for a Brain-Computer Interface, and showed that HHCRFs outperform HHMMs. In this paper, we apply HHCRFs to labeling artificial data and sports video segmentation. © 2009 Springer Berlin Heidelberg.