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 entropybased
MDL to learn optimal visual words for place classification
and categorization of the Caltech 101 object database.