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.
Moreover, in REF , multiple class object-based segmentation is achieved using the appearance and bag-of-keypoints models, integrated over meanshift patches.
Multiple Class Segmentation Using A Unified Framework over Mean-Shift Patches
2007 IEEE Conference on Computer Vision and Pattern Recognition
2007 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science Medicine