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

Authors:
Sungho Kim;In So Kweon
Affiliations:
Korea Advanced Institute of Science and Technology;Korea Advanced Institute of Science and Technology
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Year:
2006

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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.