Fine-grained bird recognition by using contour-based pose transfer

Abstract. We propose a pose transfer method for fine-grained classifications of birds that have wide variations in appearance due to different poses and subcategories. Specifically, bird pose is transferred by using Radon-transform-based contour descriptor, k-means clustering, and K nearest neighbors (KNN) classifier. During training, we clustered annotated image samples into certain poses based on their normalized part locations and used the cluster centers as their consistent part constellations for a particular pose. At the testing stage, Radon-transform-based contour descriptor is used to find the pose a sample belongs to with a KNN classifier by using cosine similarity, and normalized part constellations are transferred to the unannotated image according to the pose type. Bag-of-visual words with OpponentSIFT and color names extracted from each part and from the global image are concatenated as feature vector, which is input to support vector machine for classification. Experimental results demonstrate significant performance gains from our method on the Caltech-UCSD Birds-2011 dataset for the fine-grained bird classification task.