Abstract
Pooling layer in Convolutional Neural Networks (CNNs) is designed to reduce dimensions and computational complexity. Unfortunately, CNN is easily disturbed by noise in images when extracting features from input images. The traditional pooling layer directly samples the input feature maps without considering whether they are affected by noise, which brings about accumulated noise in the subsequent feature maps as well as undesirable network outputs. To address this issue, a robust Local Binary Pattern (LBP) Guiding Pooling (G-RLBP) mechanism is proposed in this paper to down sample the input feature maps and lower the noise impact simultaneously. The proposed G-RLBP method calculates the weighted average of all pixels in the sliding window of this pooling layer as the final results based on their corresponding probabilities of being affected by noise, thus lowers the noise impact from input images at the first several layers of the CNNs. The experimental results show that the carefully designed G-RLBP layer can successfully lower the noise impact and improve the recognition rates of the CNN models over the traditional pooling layer. The performance gain of the G-RLBP is quite remarkable when the images are severely affected by noise.
Highlights
Nowadays, using deep learning architectures to dig out information and extract features from images have drawn a lot of attention in computer vision and machine learning tasks
A new pooling method based on robust Local Binary Pattern (LBP) guiding in deep Convolutional Neural Networks (CNNs) is proposed in this paper to deal with the noise injected into the input images, which is named as RLBP
Utilizing this weight maps to down sample the convolutional feature maps, the pixels which are more likely to be affected by noise would be assigned smaller weights to lower the noise interference to the networks
Summary
Nowadays, using deep learning architectures to dig out information and extract features from images have drawn a lot of attention in computer vision and machine learning tasks. Once some of the pixels in the sliding window are affected by noise, they are still probably preserved or averaged after the pooling layer since the current pooling methods have no response to the noise injected into the input To address this issue, a new pooling method based on robust LBP guiding in deep CNNs is proposed in this paper to deal with the noise injected into the input images, which is named as RLBP. We can utilize the pattern of the pixel to guide the pooling procession to decrease the noise injected into the feature maps In this way, the parameters of the input feature maps can be reduced as the traditional pooling methods, and the impact of noise injected into the feature maps can be effectively lowered simultaneously.
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