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Spatial and temporal feature extraction using a restricted boltzmann machine model

Abstract:

A restricted Boltzmann machine (RBM) is a generative neural-network model with many applications, such as, collaborative filtering, acoustic modeling, and topic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series or video analysis. In this work we address this issue by proposing the p-RBM model: a generalization of the regular RBM model capable of retaining memory of p past states. We further show how to train the p-RBM model using contrastive divergence and test our model on the problem of recognizing human actions from video data using unsupervised feature extraction. Obtained results show that the p-RBM offers promising capabilities for feature-learning in classification applications.