Categorization in natural time-varying image sequences

Approaches to single image categorization do not easily generalize to natural time-varying image sequences. In natural environments, object categories tend to have few features that help to distinguish between each other and the surrounding environment. To better discriminate between categories and the surrounding environment, we propose a multi-view categorization approach that exploits the statistics of image sequences rather than single images. The approach is unbiased towards redundant views - that is, it does not matter how many times an object appears from the same viewpoint. At the same time, the approach does not penalize for missing views, so that we do not have to capture an object at all viewpoints to successfully categorize the object. We first present a data set for studying natural environment monitoring: an image sequence of birds at a feeder station. After manual localization, a baseline bag of features approach was found to perform significantly worse on the proposed data set compared to the standard Caltech 101 data set. We find that our approach increases the categorization accuracy from 48% to 58% on average when compared to an equivalent single view categorization method. Finally, we show how the same metric proposed for the supervised categorization can be used to transform, in an unsupervised manner, an image sequence into a manageable set of categories.