loading...
Simultaneous Classification and VisualWord Selection using Entropy-based Minimum Description Length
Hong Kong August 20-August 24
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.105518th International Conference on Patt ...
 This Article 
 
PDF
HTML
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sungho Kim, Korea Advanced Institute of Science and Technology
In So Kweon, Korea Advanced Institute of Science and Technology
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.
Citation:
Sungho Kim, In So Kweon, "Simultaneous Classification and VisualWord Selection using Entropy-based Minimum Description Length," icpr, vol. 1, pp.650-653, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006
Usage of this product signifies your acceptance of the Terms of Use.