Sciweavers

Share
ICCV
2007
IEEE

Proximity Distribution Kernels for Geometric Context in Category Recognition

9 years 7 months ago
Proximity Distribution Kernels for Geometric Context in Category Recognition
We propose using the proximity distribution of vectorquantized local feature descriptors for object and category recognition. To this end, we introduce a novel "proximity distribution kernel" that naturally combines local geometric as well as photometric information from images. It satisfies Mercer's condition and can therefore be readily combined with a support vector machine to perform visual categorization in a way that is insensitive to photometric and geometric variations, while retaining significant discriminative power. In particular, it improves on the results obtained both with geometrically unconstrained "bags of features" approaches, as well as with over-constrained "affine procrustes." Indeed, we test this approach on several challenging data sets, including Graz-01, Graz-02, and the PASCAL challenge. We registered the average performance
Haibin Ling, Stefano Soatto
Added 14 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2007
Where ICCV
Authors Haibin Ling, Stefano Soatto
Comments (0)
books