A hybrid generative/discriminative classification framework based on free-energy terms

Hybrid generative-discriminative techniques and, in particular, generative score-space classification methods have proven to be valuable approaches in tackling difficult object or scene recognition problems. A generative model over the available data for each image class is first learned, providing a relatively comprehensive statistical representation. As a result, meaningful new image features at different levels of the model become available, encoding the degree of fitness of the data with respect to the model at different levels. Such features, defining a score space, are then fed into a discriminative classifier which can exploit the intrinsic data separability. In this paper, we present a generative score-space technique which encapsulates the uncertainty present in the generative learning phase usually disregarded by the state-of-the-art methods. In particular, we propose the use of variational free energy terms as feature vectors, so that the degree of fitness of the data and t...

• Type: Journal
• Year: 2009
• Where: ICCV
• Authors: Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino, Nebojsa Jojic
• Keywords: Computer Vision, Generative Models, Generative Score-space, Generative Score-space Classification, ICCV 2009