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.

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