Preprint Article Version 1 This version is not peer-reviewed

Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

Version 1 : Received: 10 April 2017 / Approved: 11 April 2017 / Online: 11 April 2017 (10:27:10 CEST)

A peer-reviewed article of this Preprint also exists.

Zhang, M.; Hu, X.; Zhao, L.; Lv, Y.; Luo, M.; Pang, S. Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images. Remote Sens. 2017, 9, 500. Zhang, M.; Hu, X.; Zhao, L.; Lv, Y.; Luo, M.; Pang, S. Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images. Remote Sens. 2017, 9, 500.

Journal reference: Remote Sens. 2017, 9, 500
DOI: 10.3390/rs9050500

Abstract

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the manifold ranking (MR) model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (\textbf{DMSMR}) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.

Subject Areas

semantic segmentation; deep convolutional neural networks; manifold ranking; single stream optimization; high resolution image

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