CSFL: A novel unsupervised convolution neural network approach for visual pattern classification

With the advancement of technology and expansion of broadcasting around the globe has further boost up biometric surveillance systems. Pattern recognition is the key track in this area. Convolution neural network (CNN) as one of the most prevalent deep learning algorithm has gain high reputation in image features extraction. In this paper, we propose few new twists of unsupervised learning i.e. convolution sparse filter learning (CSFL) to obtain rich and discriminative features of an image. The features extracted by CSFL algorithm are used to initialize the first CNN layer, and then these features are further used in feed forward manner by the CNN to learn high level features for classification. The linear regression classifier (softmax classifier) is used to serve as the output layer of CNN for providing the probability of an image class. We present and examine five different architectures of CNN and error function mean square error (MSE). The experimental results on a public dataset showcase the merit of the proposed method.