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We propose an architecture for fine-grained visual categorization thatapproaches expert human performance in the classification of bird species. Ourarchitecture first computes an estimate of the object's pose; this is used tocompute local image features which are, in turn, used for classification. Thefeatures are computed by applying deep convolutional nets to image patches thatare located and normalized by the pose. We perform an empirical study of anumber of pose normalization schemes, including an investigation of higherorder geometric warping functions. We propose a novel graph-based clusteringalgorithm for learning a compact pose normalization space. We perform adetailed investigation of state-of-the-art deep convolutional featureimplementations and fine-tuning feature learning for fine-grainedclassification. We observe that a model that integrates lower-level featurelayers with pose-normalized extraction routines and higher-level feature layerswith unaligned image features works best. Our experiments advancestate-of-the-art performance on bird species recognition, with a largeimprovement of correct classification rates over previous methods (75% vs.55-65%).

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets