Shape Modeling Based on Convolutional Restricted Boltzmann Machines

摘要

This paper proposes a kind of shape model based on convolutional restricted Boltzmann machines(CRBM), which can be used to assist the task of image target detection and classification. The CRBM is a generative model that can model shapes through the generative capabilities of the model. This paper presents the visual representation, construction process and training method of the model construction. This paper does experiments on the Weizmann Horse dataset. The results show that, compared with RBM, although the training time of this model is slightly longer, the test time of the model is similar, and it can better shape modeling, modeling of the details of the shape can be well expressed. The samples generated from CRBM look more realistic. The difference between the shape and the original shape generated by Euclidean distance measurement shows that the model has a strong ability to model shapes.