Research and Application of Deep Belief Network Based on Local Binary Pattern and Improved Weight Initialization

In order to extract the features of the image more accurately, a deep belief network (DBN) based image feature extraction method is proposed. However, when the deep belief network extracts the features of the image, it is easy to ignore the local texture features of the image. Then the block local local binary mode is introduced to extract the local texture features of the image. At the same time, to improve the slow learning speed of the network, the initial weight of the network is improved. Finally, the proposed network is tested on the ORL image dataset. The results show that the proposed method not only improves the recognition accuracy of the network, but also accelerates the convergence speed of the network to some extent.