Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single global inner center and a set of radii corresponding to a polar coordinate parameterization. In this paper, we propose AdaContour, an adaptive contour descriptor that uses multiple local representations to desirably characterize complex shapes. After hierarchically encoding object shapes in a training set and constructing a contour matrix of all subdivided regions, we compute a robust low-rank robust subspace and approximate each local contour by linearly combining the shared basis vectors to represent an object. Experiments show that AdaContour is able to represent shapes more accurately and robustly than other descriptors while retaining effectiveness. We validate AdaContour by integrating it into off-the-shelf detectors to enable instance segmentation which demonstrates faithful performance. The code is available at https://github.com/tding1/AdaContour.

翻译：现有的基于角度的轮廓描述符在处理非星形凸形状时存在有损表示的问题。总体而言，这是由于形状通过单一全局内部中心及一组对应于极坐标参数化的半径进行配准所导致的。本文提出AdaContour，一种自适应轮廓描述符，通过使用多个局部表示来理想地刻画复杂形状。在层次化编码训练集中的物体形状并构建所有细分区域的轮廓矩阵后，我们计算出一个稳健的低秩子空间，并通过线性组合共享基向量来近似每个局部轮廓以表示物体。实验表明，AdaContour能够比其他描述符更精确、更鲁棒地表示形状，同时保持有效性。我们通过将AdaContour集成到现成检测器中实现实例分割来验证其性能，结果展现出可靠的表现。代码可见于https://github.com/tding1/AdaContour。