Harmonium Models for Semantic Video Representation and Classification

Accurate and efficient video classification demands the fusion of multimodal information and the use of intermediate representations. Combining the two ideas into the same framework, we propose a probabilistic approach for video classification using intermediate semantic representations derived from the multi-modal features. Based on a class of bipartite undirected graphical models named harmonium, our approach represents video data as latent semantic topics derived by jointly modeling the transcript keywords and color-histogram features, and perform classification using these latent topics under a unified framework. We show satisfactory classification performance of our approach on a benchmark dataset, and some interesting insights of the data provided by this approach.