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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 3792805, 12 pages
Research Article

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

1College of IOT Engineering, Hohai University, Changzhou 213022, China
2Key Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, China

Correspondence should be addressed to Qingwu Li; moc.361@uwgniq_il

Received 1 August 2016; Revised 28 November 2016; Accepted 15 January 2017; Published 16 February 2017

Academic Editor: Leonardo Franco

Copyright © 2017 Liangji Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.