Fine-Grained Visual Categorization with 2D-Warping

The task of fine-grained visual categorization is related to both general object recognition and specialized tasks such as face recognition. Hence, we propose to combine two methods popular for general object recognition and face recognition to build a new model-free system for fine-grained visual categorization. Specifically, we use Local Naive-Bayes Nearest Neighbor as a pre-selection method and 2D-Warping as a refinement step. For the latter, we explore different ways to use the alignments computed by a 2D-Warping algorithm for classification. We demonstrate the performance of our approach on the CUB200-2011 database and show that our approach outperforms the recognition accuracy of current state-of-the-art methods.