Simultaneous Classification and VisualWord Selection using Entropy-based Minimum Description Length
In this paper, we present a new entropy-based minimum description length (MDL) criterion for simultaneous classification and visual word selection. Conventional MDL criteria focus on how to minimize cluster size and maximize the likelihood of data points. We extend the MDL by replacing the likelihood term with the entropy of class posterior. This new criterion can provide optimal visual words with enough classification accuracy. We validate the entropy-based MDL to learn optimal visual words for place classification and categorization of the Caltech 101 object database