Multiple class segmentation using a unified framework over mean-shift
patches.
Lin Yang(1),(2), Peter Meer(1), David J. Foran(2)
(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(2)BioImaging Laboratory, Department of Pathology and Laboratory Medicine
UMDNJ-Robert Wood Johnson Medical School
Piscataway, NJ 08855, USA
Object-based segmentation is a challenging topic. Most of the
previous algorithms focused on segmenting a single or a small set of
objects. In this paper, the multiple class object-based segmentation
is achieved using the appearance and bag of keypoints models
integrated over mean-shift patches. We also propose a novel affine
invariant descriptor to model the spatial relationship of keypoints
and apply the Elliptical Fourier Descriptor to describe the global
shapes. The algorithm is computationally efficient and has been
tested for three real datasets using less training samples. Our
algorithm provides better results than other studies reported in the
literature.
2007 Computer Vision and Pattern Recognition Conference,
Minneapolis, Minnesota, June 2007.
