We approach the classification problem in a discrim- inative setting, as learning a max-margin classifier that infers the class label along with the latent variables. Through this paper we make the following contribu- tions: a) we provide a method for incorporating latent variables into object and action classification; b) these variables determine the relative focus on foreground vs. background information that is taken account of; c) we design an objective function to more effectively learn in unbalanced data sets; d) we learn a better classifier by iterative expansion of the latent parameter space. We demonstrate the performance of our approach through
Recommended citation: V. De Smet, L. Van Gool and V. P. Namboodiri, “Nonuniform image patch exemplars for low level vision,” 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, 2013, pp. 23-30.