Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features.
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.
College of IOT Engineering, Hohai University, Changzhou 213022, China.
Liangji
0000-0002-6257-550X
L
Zhou
College of IOT Engineering, Hohai University, Changzhou 213022, China; Key Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, China.
Qingwu
0000-0003-3224-9831
Q
Li
College of IOT Engineering, Hohai University, Changzhou 213022, China; Key Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, China.
Guanying
G
Huo
College of IOT Engineering, Hohai University, Changzhou 213022, China; Key Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, China.
Yan
0000-0002-3213-8229
Y
Zhou
D000818:Animals; D032701:Biomimetics; D003952:Diagnostic Imaging / Q000145:classification*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated