Evidential Object Recognition Based on Information Gain Maximization

This paper presents an object recognition approach based on belief function inference and information gain maximization. A common problem for probabilistic object recognition models is that the parameters of the probability distributions cannot be accurately estimated using the available training data due to high dimensionality. We therefore use belief functions in order to make the reliability of the evidence provided by the training data an explicit part of the recognition model. In contrast to typical classification approaches, we consider recognition as a sequential information-gathering process where a system with dynamic beliefs actively seeks to acquire new evidence. This acquisition process is based on the principle of maximum expected information gain and enables the system to perform optimal actions for reducing uncertainty as quickly as possible. We evaluate our system on a standard object recognition dataset where we investigate the effect of the amount of training data on classification performance by comparing different methods for constructing belief functions from data.