Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to re...Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to realize accurate recognition. To solve this problem, a mobile phone recognition method based on bilinear-convolutional neural network(B-CNN) is proposed in this paper.First, a feature extraction model, based on B-CNN, is designed to adaptively extract local features from the images of secondhand mobile phones. Second, a joint loss function, constructed by center distance and softmax, is developed to reduce the interclass feature distance during the training process. Third, a parameter downscaling method, derived from the kernel discriminant analysis algorithm, is introduced to eliminate redundant features in B-CNN. Finally, the experimental results demonstrate that the B-CNN method can achieve higher accuracy than some existing methods.展开更多
基金supported by the National Key Program of China(Grant No.2018YFC1900800-5)the National Natural Science Foundation of China(Grant Nos.61890930-5 and 61622301)the Beijing University Outstanding Young Scientist Program(Grant No.BJJWZYJH0120191000-5020)。
文摘Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to realize accurate recognition. To solve this problem, a mobile phone recognition method based on bilinear-convolutional neural network(B-CNN) is proposed in this paper.First, a feature extraction model, based on B-CNN, is designed to adaptively extract local features from the images of secondhand mobile phones. Second, a joint loss function, constructed by center distance and softmax, is developed to reduce the interclass feature distance during the training process. Third, a parameter downscaling method, derived from the kernel discriminant analysis algorithm, is introduced to eliminate redundant features in B-CNN. Finally, the experimental results demonstrate that the B-CNN method can achieve higher accuracy than some existing methods.
