Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the M eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more efficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningful performances on instance segmentation datasets.
@article{arxiv.2203.15259,
title = {Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation},
author = {Wonhui Park and Dongkwon Jin and Chang-Su Kim},
journal= {arXiv preprint arXiv:2203.15259},
year = {2022}
}