A Method of Deep Belief Network Image Classification Based on Probability Measure Rough Set Theory

Abstract

The increasing diverse demand for image feature recognition and complicated relationships among image pixels cannot be fully and effectively handled by traditional single image recognition methods. In order to effectively improve classification accuracy in image processing, a deep belief network (DBN) classification model based on probability measure rough set theory is proposed in our research. First, the incomplete and inaccurate fuzzy information in the original image is preprocessed by the rough set method based on probability measure. Second, the attribute features of the image information are extracted, the attribute feature set is reduced to generate the classification rules, and key components are extracted as the input of the DBN. Third, the network structure of the DBN is determined by the extracted classification rules, and the importance of the rough set attributes is integrated and the weights of the neuronal nodes are corrected by the backpropagation (BP) algorithm. Last, the DBN is trained to classify images. The experimental analysis of the proposed method for medical imagery shows that it is more effective than current single rough set approach or the taxonomy of deep learning.