The two stages hierarchical unsupervised learning system for complex dynamic scene recognition

The two stage hierarchical unsupervised learning system has been proposed for modeling complex dynamic surveillance and cyberspace systems. Using a modification of the expectation maximization learning approach, we introduced a three layer approach to learning concepts from input data: features, objects, and situations. Using the Bernoulli model, this approach models each situation as a collection of objects, and each object as a collection of features. Further complexity is added with the addition of clutter features and clutter objects. During the learning process, at the lowest level, only binary feature information (presence or absence) is provided. The system attempts to simultaneously determine the probabilities of the situation and presence of corresponding objects from the detected features. The proposed approach demonstrated robust performance after a short training period. This paper discusses this hierarchical learning system in a broader context of different feedback mechanisms between layers and highlights challenges on the road to practical applications.