Conditional feature sensitivity: a unifying view on active recognition and feature selection

The objective of active recognition is to iteratively collect the next "best" measurements (e.g., camera angles or viewpoints), to maximally reduce ambiguities in recognition. However, existing work largely overlooked feature interaction issues. Feature selection, on the other hand, focuses on the selection of a subset of measurements for a given classification task, but is not context sensitive (i.e., the decision does not depend on the current input). This paper proposes a unified perspective through conditional feature sensitivity analysis, taking into account both current context and feature interactions. Based on different representations of the contextual uncertainties, we present three treatment models and exploit their joint power for dealing with complex feature interactions. Synthetic examples are used to systematically test the validity of the proposed models. A practical application in medical domain is illustrated using an echocardiography database with more than 2000 video segments with both subjective (from experts) and objective validations.