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SIBGRAPI
2009
IEEE

Learning Discriminative Appearance-Based Models Using Partial Least Squares

9 years 1 months ago
Learning Discriminative Appearance-Based Models Using Partial Least Squares
Appearance information is essential for applications such as tracking and people recognition. One of the main problems of using appearance-based discriminative models is the ambiguities among classes when the number of persons being considered increases. To reduce the amount of ambiguity, we propose the use of a rich set of feature descriptors based on color, textures and edges. Another issue regarding appearance modeling is the limited number of training samples available for each appearance. The discriminative models are created using a powerful statistical tool called Partial Least Squares (PLS), responsible for weighting the features according to their discriminative power for each different appearance. The experimental results, based on appearance-based person recognition, demonstrate that the use of an enriched feature set analyzed by PLS reduces the ambiguity among different appearances and provides higher recognition rates when compared to other machine learning techniques.
William Robson Schwartz, Larry S. Davis
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where SIBGRAPI
Authors William Robson Schwartz, Larry S. Davis
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