Learning Discriminative Appearance-Based Models Using Partial Least Squares
Top Cited Papers
- 1 October 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 322-329
- https://doi.org/10.1109/sibgrapi.2009.42
Abstract
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.Keywords
This publication has 16 references indexed in Scilit:
- Human detection using partial least squares analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Real-time Accurate Object Detection using Multiple ResolutionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Person Reidentification Using Spatiotemporal AppearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Overview and Recent Advances in Partial Least SquaresLecture Notes in Computer Science, 2006
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Probabilistic tracking in joint feature-spatial spacesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Full-body person recognition systemPattern Recognition, 2003
- Kernel-based object trackingIEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
- Joint induction of shape features and tree classifiersIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973