A Novel Gaussian–Bernoulli Based Convolutional Deep Belief Networks for Image Feature Extraction

Image feature extraction is an essential step in the procedure of image recognition. In this paper, for images features extracting and recognizing, a novel deep neural network called Gaussian–Bernoulli based Convolutional Deep Belief Network (GCDBN) is proposed. The architecture of the proposed GCDBN consists of several convolutional layers based on Gaussian–Bernoulli Restricted Boltzmann Machine. This architecture can take the advantages of Gaussian-Binary Restricted Boltzmann machine and Convolutional Neural Network. Each convolutional layer is followed by a stochastic pooling layer for down-sampling the feature maps. We evaluated our proposed model on several image benchmarks. The experimental results show that our model is more effective for most of images recognition tasks with comparably low computational cost than some of popular methods, which is suggested that our proposed deep network is a potentially applicable method for real-world image recognition.