Unsupervised Segmentation Incorporating Shape Prior via Generative Adversarial Networks

Dahye Kim, Byung-Woo Hong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7324-7334

We present an image segmentation algorithm that is developed in an unsupervised deep learning framework. The delineation of object boundaries often fails due to the nuisance factors such as illumination changes and occlusions. Thus, we initially propose an unsupervised image decomposition algorithm to obtain an intrinsic representation that is robust with respect to undesirable bias fields based on a multiplicative image model. The obtained intrinsic image is subsequently provided to an unsupervised segmentation procedure that is developed based on a piecewise smooth model. The segmentation model is further designed to incorporate a geometric constraint imposed in the generative adversarial network framework where the discrepancy between the distribution of partitioning functions and the distribution of prior shapes is minimized. We demonstrate the effectiveness and robustness of the proposed algorithm in particular with bias fields and occlusions using simple yet illustrative synthetic examples and a benchmark dataset for image segmentation.