Clustering Appearances of Objects Under Varying Illumination Conditions
Jeffrey Ho, Ming-Hsuan Yang, Jongwoo Lim, Kuang-Chih Lee, David Kriegman
CVPR 2003, vol 1, pp 11-18
We introduce two appearance-based methods for clustering
a set of images of 3-D objects, acquired under varying illumination
conditions, into disjoint subsets corresponding
to individual objects. The first algorithm is based on the
concept of illumination cones. According to the theory, the
clustering problem is equivalent to finding convex polyhedral
cones in the high-dimensional image space. To effi-
ciently determine the conic structures hidden in the image
data, we introduce the concept of conic affinity which measures
the likelihood of a pair of images belonging to the
same underlying polyhedral cone. For the second method,
we introduce another affinity measure based on image gradient
comparisons. The algorithm operates directly on the
image gradients by comparing the magnitudes and orientations
of the image gradient at each pixel. Both methods have
clear geometric motivations, and they operate directly on
the images without the need for feature extraction or computation
of pixel statistics. We demonstrate experimentally
that both algorithms are surprisingly effective in clustering
images acquired under varying illumination conditions with
two large, well-known image data sets.