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Indexed by:会议论文

Date of Publication:2018-01-01

Included Journals:CPCI-S

Volume:10878

Page Number:568-577

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

Abstract: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.