Object Segmentation using Parametric Representation - Rhee, Hochang; Koo, Hyung Il; Cho, Nam Ik (Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022, 2022)
Most deep learning-based segmentation methods focus on accurate pixel-wise classification, disregarding higher order properties such as region connectivity, boundary smoothness, or the number of non-differentiable points on the object contour. To address these issues, we propose a new parameter-based object segmentation framework. Specifically, we represent the target object's boundary with parametric curves to handle the higher-order properties and find the parameters through a convolutional neural network. We also introduce a novel silhouette loss to train the network, enabling efficient parameter-based contour fitting. The proposed silhouette loss is based on a differentiable renderer and is suitable for the segmentation task because it has the same property as IoU (intersection over union). Experimental results show that the proposed method yields object masks with desirable properties and achieves comparable performance to the state-of-the-art on various tasks.
Keywords: Boundary smoothness; High-order; Higher-order; Learning-based segmentation; Non-differentiable points; Objects segmentation; Parametric representations; Property; Region connectivities; Segmentation methods