Enhanced pooling method for convolutional neural networks based on optimal search theory

Abstract

To obtain the best pooling effect and higher accuracy in image recognition, an improved method based on optimal search theory for the pooling layer of convolutional neural networks (CNNs) is proposed. The purpose is to solve the problems of the traditional pooling method, namely that it is too simplistic and it is difficult to extract effective features. The basic principle and network structure of CNN are introduced in the study. A new optimum-pooling method is proposed, and the authors study how to obtain the maximum probability to detect the target function under the constrained condition. Comparison experiments of different pooling methods are performed on three widely used datasets: LFW, CIFAR-10, and ImageNet. The experimental results show that the proposed method has the characteristics of more effective feature extraction and wide adaptability, and leads to higher accuracy and lower error rate in image recognition.