Lookup NU author(s): Xin Lai,
Dr Mohsen Naqvi,
Professor Jonathon Chambers
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To obtain the best pooling effect and higher accuracy in image recognition, an improved method based on optimalsearch theory for the pooling layer of convolutional neural networks (CNNs) is proposed. The purpose is to solve the problemsof the traditional pooling method, namely that it is too simplistic and it is difficult to extract effective features. The basic principleand network structure of a CNN are introduced in the paper. A new optimum-pooling method is proposed, and we study how toobtain the maximum probability to detect the target function under the constrained condition. Comparison experiments of differentpooling methods are performed on three widely used datasets: LFW, CIFAR-10, and ImageNet. The experimental results showthat the proposed method has the characteristics of more effective feature extraction and wide adaptability, and leads to higheraccuracy and lower error rate in image recognition.
Author(s): Lai X, Zhou L, Fu Z, Naqvi SM, Chambers J
Publication type: Article
Publication status: Published
Journal: IET Image Processing
Pages: epub ahead of print
Online publication date: 01/08/2019
Acceptance date: 16/07/2019
ISSN (print): 1751-9659
ISSN (electronic): 1751-9667
Publisher: The Institution of Engineering and Technology
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