论文标题
Eigencontours：基于低级别近似的新型轮廓描述符
Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation
论文作者
论文摘要
本文提出了基于低级别近似的新型轮廓描述符，称为Eigencontours。首先，我们构建一个轮廓矩阵，该矩阵包含训练集中的所有对象边界。其次，我们通过最佳秩-M近似将轮廓矩阵分解为eigencontours。第三，我们通过m eigencontours的线性组合表示对象边界。我们还将Eigencontours纳入实例分割框架中。实验结果表明，与低维空间中现有的描述符相比，所提出的Eigencontours可以更有效，更有效地表示对象边界。此外，所提出的算法在实例分割数据集上得出有意义的性能。
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.
