Banknote Classification Based on Convolutional Neural Network in Quaternion Wavelet Domain

Abstract

In this paper, we propose a new framework for banknote classification based on quaternion wavelet transform (QWT) and deep convolutional neural network. Firstly, the QWT is applied to describe the phase and magnitude of different banknote images which has inherent directional sensitivity and multi-scale framework. Then we design a deep convolutional neural network which is trained on banknote images along with the magnitude and phase of quaternion wavelet coefficients. We assign the neural weights on the output probabilities of deep convolutional neural network and update these weights by utilizing the back propagation algorithm. Finally, the trained networks with decision of a weighted sum and the magnitude and the phase of quaternion wavelet networks are utilized for banknote image classification. The performance of our algorithm is experimentally verified on a variety of banknote databases. Experimental results show that the proposed algorithm achieves superior performance compared with other state-of-the-art banknote classification algorithms. The proposed algorithm can also satisfy the real-time requirements of the banknote sorting system. INDEX TERMS Quaternion wavelet transform, Convolutional neural network, Banknote classification, mixture model.

A novel non-linear neuron model based on multiplicative aggregation in quaternionic domain

“…Apart from these, quaternion-valued neural networks (QVNN) store and learn the spatial relationships in the various transformations of 3D coordinates [2,3] and in between the color pixels [26], whereas real/complexvalued neural network fails. These qualities have motivated the researchers to apply QVNN in many fields such as automatic speech recognition [27,28], image classification [29], PolSAR land classification [30], prostate cancer Gleason grading [31], color image compression [32], facial expression recognition [33], robot manipulator [34], spoken language understanding [35], attitude control of spacecraft [36], and banknote classification [37]. However, 3D or 4D information has been processed using a real-valued neural network (RVNN) where all components are considered separately in which it is neglected the correlation among each other, addressed in [38].…”