Simultaneous Classification and VisualWord Selection using Entropy-based Minimum Description Length

Abstract

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.