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论文类型：会议论文

发表时间：2018-01-01

收录刊物：CPCI-S

卷号：10878

页面范围：568-577

关键字：Bird species recognition; Convolutional neural network (CNN); Object detection; High dimensional Gaussian descriptors

摘要：Bird species recognition is one of the most challenging tasks in fine-grained visual categorizations (FGVC) and has attracted wide attention in recent years. In this paper, we develop a bird recognition system that consists of three learning-type computational modules: the first one is for extracting the object and key parts from input images that is implemented by training a deep convolutional neural network (CNN) model, the second module is for feature extraction from the detected object and parts by using four other CNNs and further for computation of the high dimensional Gaussian descriptors based on the deep features, and the third module is for getting the final recognition result that is implemented by training four SVM classifiers with the Gaussian descriptors and integrating the outputs of the SVMs together with a decision fusion method. Experiment results obtained in the paper confirm the validity of the proposed approach.