Adaptive Resolution Selection for Improving Segmentation Accuracy of Small Objects

Haruki Fujii, Kazuhiro Hotta

2023

Abstract

This paper proposes a segmentation method using adaptive resolution selection for improving the accuracy of small objects. In semantic segmentation, the segmentation of small objects is more difficult than that of large objects. Semantic segmentation requires both spatial details to locate objects and strong semantics to classify objects well, which are likely to exist at different resolution/scale levels. We believe that small objects are well represented by high-resolution feature maps, while large objects are suitable for low-resolution feature maps with high semantic information, and propose a method to automatically select a resolution and assign it to each object in the HRNet with multi-resolution feature maps. We propose Adaptive Resolution Selection Module (ARSM), which selects the resolution for segmentation of each class. The proposed method considers the feature map of each resolution in the HRNet as an Expert Network, and a Gating Network selects adequate resolution for each class. We conducted experiments on Drosophila cell images and the Covid 19 dataset, and confirmed that the proposed method achieved higher accuracy than the conventional method.

Paper Citation

in Harvard Style

Fujii H. and Hotta K. (2023). Adaptive Resolution Selection for Improving Segmentation Accuracy of Small Objects. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 393-400. DOI: 10.5220/0011736800003417

in Bibtex Style

@conference{visapp23,
author={Haruki Fujii and Kazuhiro Hotta},
title={Adaptive Resolution Selection for Improving Segmentation Accuracy of Small Objects},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={393-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011736800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Adaptive Resolution Selection for Improving Segmentation Accuracy of Small Objects
SN - 978-989-758-634-7
AU - Fujii H.
AU - Hotta K.
PY - 2023
SP - 393
EP - 400
DO - 10.5220/0011736800003417
PB - SciTePress