Experiental sampling in multimedia systems

Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many real-time applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it to build the model of the environment. We formulate this problem as an experiential sampling problem and propose an approach to utilize computing resources efficiently on the most informative subset of data streams. First, in this paper, we focus on theoretical background and develop a theoretical framework for a single data stream. We generalize the notion of static visual attention in a dynamical systems setting and propose a dynamical attention-orientated analysis method. This is achieved by a sampling representation that utilizes the current context and past experience for attention evolution. Hence, the multimedia analysis task at hand can select its data of interest while immediately discarding the irrelevant data to achieve efficiency and adaptability.