FuSS: Fusing Superpixels for Improved Segmentation Consistency

• URL: http://arxiv.org/abs/2206.02714v1
• Date: Mon, 6 Jun 2022 16:14:19 GMT
• Title: FuSS: Fusing Superpixels for Improved Segmentation Consistency
• Authors: Ian Nunes, Matheus B. Pereira, Hugo Oliveira, Jefersson A. Dos Santos and Marcus Poggi
• Abstract summary: We propose two approaches to improve the semantic consistency of Open Set Semantic. First, we propose a method called OpenGMM to model the distribution of pixels for each class in a multimodal manner. The second approach is a post-processing which uses superpixels to enforce highly homogeneous regions to behave equally.
• Score: 2.7771471571972333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of Models to model the distribution of pixels for each class in a multimodal manner. The second approach is a post-processing which uses superpixels to enforce highly homogeneous regions to behave equally, rectifying erroneous classified pixels within these regions, we also proposed a novel superpixel method called FuSS. All tests were performed on ISPRS Vaihingen and Potsdam datasets, and both methods were capable to improve quantitative and qualitative results for both datasets. Besides that, the post-process with FuSS achieved state-of-the-art results for both datasets. The official implementation is available at: \url{https://github.com/iannunes/FuSS}.

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