Aggregating region context information for semantic segmentation

Zilin Guo; Dongyue Wu; Qiang Zhang; Nong Sang; Changqian Yu; Changxin Gao

doi:10.1117/12.2623426

16 February 2022 Aggregating region context information for semantic segmentation

Zilin Guo, Dongyue Wu, Qiang Zhang, Nong Sang, Changqian Yu, Changxin Gao

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Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208307 (2022) https://doi.org/10.1117/12.2623426
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Recently, lots of works try to capture contextual information to benefit semantic segmentation problems. However, most approaches adopt the uniform method to obtain context information, which means each pixel gets its context from the same region. We argue that for each pixel, contextual information aggregated from the region it belongs to can benefit the dense prediction, while those from other irrelevant regions possibly mislead the prediction. In this work, we propose a Region Context Module (RCM) that aggregates context for each pixel only from its object region. Furthermore, we design a Region Context Network (RCNet) embedded in the ASPP Module and Region Context Module. We conduct experiments on three datasets: Cityscapes, Vaihingen and Potsdam datasets. Extensive quantitative and qualitative evaluations demonstrate our model achieves favorable performance against state-of-the-art approaches.

Citation Download Citation

Zilin Guo, Dongyue Wu, Qiang Zhang, Nong Sang, Changqian Yu, and Changxin Gao "Aggregating region context information for semantic segmentation", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208307 (16 February 2022); https://doi.org/10.1117/12.2623426

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KEYWORDS

Image segmentation

RGB color model

Visualization

Binary data

Convolution

Network architectures

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