Abstract
A novel deep architecture bilateral filter NIN for classification tasks is proposed in the paper, in which the input image pixels using the bilateral filter and a multi-path convolution neural network are reconstructed. This network has two input paths, one is the original image and the other is the reconstructed image which independent on and complement each other. Therefore, the loss of foreground object texture and shape information can be reduced during the process of feature extraction from the complex background images. Then, the softmax classifier is employed to classify the extracted features. Experiments are demonstrated on CAFIR-100 dataset, in which some object’s feature gradually disappear after pass through a series of convolution layers and average pooling layers. The results show that, Compared with NIN(network in net- work), the classification accuracy rate increased 0.6% on CIFAR-10 database, accuracy rate increased 0.27% on cifar-100 database.
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Dong, J., Gao, Y., Li, H., Guo, T. (2017). Bilateral Filtering NIN Network for Image Classification. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_58
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DOI: https://doi.org/10.1007/978-3-319-63312-1_58
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