Learning Discriminative Appearance-Based Models Using Partial Least Squares

• Published in: 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing pp. 322 - 329
• Main Authors: Schwartz, W.R., Davis, L.S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2009
• Subjects: Appearance-Based Models; Co-occurrence matrices; Computer graphics; Data mining; Filtering; HOG; Image analysis; Image processing; Informatics; Least squares methods; Partial Least Squares; PLS; Signal analysis; Signal processing; Wavelet analysis
• ISBN: 1424449782; 9781424449781
• ISSN: 1530-1834
• DOI: 10.1109/SIBGRAPI.2009.42

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.