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ICPR
2006
IEEE

Forest Extension of Error Correcting Output Codes and Boosted Landmarks

15 years 1 months ago
Forest Extension of Error Correcting Output Codes and Boosted Landmarks
In this paper, we introduce a robust novel approach for detecting objects category in cluttered scenes by generating boosted contextual descriptors of landmarks. In particular, our method avoids the need of image segmentation, being at the same time invariant to scale, global illumination, occlusions and to small affine transformations. Once detected the object category, we address the problem of multiclass recognition where a battery of classifiers is trained able to capture the shared properties between the object descriptors across classes. A natural way to address the multiclass problem is using the Error Correcting Output Codes technique. We extend the ECOC technique proposing a methodology to construct a forest of decision trees that are included in the ECOC framework. We present very promising results on standard databases: UCI database and Caltech database as well as in a real image problem.
Oriol Pujol, Petia Radeva, Sergio Escalera
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Oriol Pujol, Petia Radeva, Sergio Escalera
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