Probabilistic Diffusion Classifiers for Object Detection
This paper presents a stochastic diffusion approach to prototype-based classification. Relations between exemplary objects and their features are modeled in a bipartite graph. A Bayesian interpretation of the model leads to a Markov chain over the set of objects. In contrast to related graph diffusion approaches, our dual treatment of objects and features easily copes with out of sample objects. Applied to problems in color object localization in unconstrained images, our method performs robust and yields promising results.