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

Author

Kim, Sungho ; Kweon, In So

Author_Institution

Korea Adv. Inst. of Sci. & Technol., Daejeon

Volume

1

fYear

0

fDate

0-0 0

Firstpage

650

Lastpage

653

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 entropy-based MDL to learn optimal visual words for place classification and categorization of the Caltech 101 object database

Keywords

feature extraction; image classification; learning (artificial intelligence); Caltech 101 object database; entropy-based minimum description length; object categorization; topological place classification; visual word selection; Bayesian methods; Clustering methods; Computer vision; Entropy; Feature extraction; Layout; Learning systems; Pattern recognition; Spatial databases; Visual databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.1055

Filename

1698976