Fuzzy based Pooling in Convolutional Neural Network for Image Classification

This paper introduces a novel pooling method namely fuzzy based pooling for image classification. Herein, a pooling method for bolstering the performance of conventional convolutional neural network (CNN) has been proposed. Conventional architecture of CNN uses pooling operation for dimension reduction which sometimes results in the loss of information. In this paper, a novel pooling method using fuzzy logic is introduced for dimension reduction. The proposed pooling method reduces the spatial size of convolved features in two steps. In the first step, the convolved features within a window to be pooled are processed using type-2 fuzzy logic for identifying the dominant features. Then, type-1 fuzzy logic with a weighted average of the dominant features within a window is used to reduce the spatial size. The proposed method is bench marked against conventional pooling techniques for MNIST dataset of handwritten digits recognition and CIFAR-10 dataset of RGB images. The accuracy shows that the proposed fuzzy based pooling performs better than the standard pooling techniques such as max and average pooling which helps to improve the performance of CNN.