Mixed fuzzy pooling in convolutional neural networks for image classification.

Convolutional Neural Networks (CNN) are being widely practised in computer vision applications, where pooling indicates as an integral part. Pooling significantly reduces the training time and computational cost of any network. The pooling operations generally being used are max, average, or stochastic. The performance of the CNN architecture can be increased by using more than one pooling operations. Herein, a novel mixed fuzzy pooling is proposed for image classification in the CNN architecture. In the proposed mixed fuzzy pooling, max pooling and fuzzy pooling are combined together to boost the classification accuracy. The proposed mixed fuzzy pooling is designed as a function of max pooling, fuzzy pooling, and α parameter where the learning of the proposed pooling function takes place with the value of α. In fuzzy pooling, Type-2 fuzzy logic is used to get the convolved dominant features. This is obtained by the estimation of a threshold for image region to be pooled. Thereafter, Type-1 fuzzy logic is used to get the pooling output by a weighted average of convolved dominant features. The performance of various pooling strategies on MNIST dataset for handwritten digits classification and CIFAR-10 dataset for RGB images classification have been compared and analysed. The experimental results show better image classification accuracy for the proposed mixed fuzzy pooling than conventional pooling operations. [ABSTRACT FROM AUTHOR]