Selective parts for fine-grained recognition

Classical visual bag-of-words approaches tackle fine-grained recognition using global features which discard spatial location of features. In this paper, we propose a novel part-based approach to distinguish fine-grained categories. This work is distinguished by two contributions. First, a fully automatic technique for selecting mid-level parts from large amounts of candidate regions without any part supervised information is presented. We call the selected parts by discriminative mining algorithm as selective parts. Second, a general effective evaluation criterion of quantifying part discriminability is built, which leads to joint selection process. For classification, feature ensembles are constructed based on global object and selective parts. Experimental results demonstrate the particular effectiveness of selective parts for fine-grained recognition on bird species on the Caltech UCSD Birds (CUB) dataset.