1 research outputs found
Volume Preserving Image Segmentation with Entropic Regularization Optimal Transport and Its Applications in Deep Learning
Image segmentation with a volume constraint is an important prior for many
real applications. In this work, we present a novel volume preserving image
segmentation algorithm, which is based on the framework of entropic regularized
optimal transport theory. The classical Total Variation (TV) regularizer and
volume preserving are integrated into a regularized optimal transport model,
and the volume and classification constraints can be regarded as two measures
preserving constraints in the optimal transport problem. By studying the dual
problem, we develop a simple and efficient dual algorithm for our model.
Moreover, to be different from many variational based image segmentation
algorithms, the proposed algorithm can be directly unrolled to a new Volume
Preserving and TV regularized softmax (VPTV-softmax) layer for semantic
segmentation in the popular Deep Convolution Neural Network (DCNN). The
experiment results show that our proposed model is very competitive and can
improve the performance of many semantic segmentation nets such as the popular
U-net