Deep Multiphase Level Set for Scene Parsing

TL;DR

This paper introduces a novel Deep Multiphase Level Set method that combines deep neural networks with multiphase level sets to improve boundary accuracy in scene parsing, achieving state-of-the-art results.

Contribution

It integrates multiphase level sets into deep learning for semantic segmentation, addressing boundary accuracy issues of FCNs with a novel, efficient approach.

Findings

Achieves new state-of-the-art performance on three benchmarks.

Effectively combines deep learning with level set methods for boundary precision.

Demonstrates robustness across multiple datasets.

Abstract

Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to discriminate pixels around the object boundaries, thus FCN based methods may output parsing results with inaccurate boundaries. Meanwhile, level set based active contours are superior to the boundary estimation due to the sub-pixel accuracy that they achieve. However, they are quite sensitive to initial settings. To address these limitations, in this paper we propose a novel Deep Multiphase Level Set (DMLS) method for semantic scene parsing, which efficiently incorporates multiphase level sets into deep neural networks. The proposed method consists of three modules, i.e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning. More specifically, recurrent FCNs learn multi-level representations of input images with different contexts. Adaptive multiphase level set drives the discriminative contour for each semantic class, which makes use of the advantages of both global and local information. In each time-step of the recurrent FCNs, deeply supervised learning is incorporated for model training. Extensive experiments on three public benchmarks have shown that our proposed method achieves new state-of-the-art performances.