Clustered Exemplar-SVM: Discovering sub-categories for visual recognition

We present a novel algorithm for image classification that is targeted to capture class variability. A single model is often not sufficient to represent a category since categories can vary from large semantic classes to fine-grained sub-categories. Instead, we develop a representation based on discovering visually similar sub-categories within a given class. We introduce a novel Clustered Exemplar SVM classifier which incorporates data-driven and exemplar focused discovery. Semi-supervised learning is employed for training each C-eSVM classifier. We evaluate our approach on two datasets and demonstrate the efficacy of our method over standard Exemplar SVM.