计算机与数字工程2024，Vol.52Issue(2)：572-577,6.DOI:10.3969/j.issn.1672-9722.2024.02.049
基于候选区域生成的弱监督图像语义分割算法
Weakly-supervised Image Semantic Segmentation Algorithm Based on Candidate Regions
王祎 1汪洋2
作者信息
- 1. 武汉邮电科学研究院 武汉 430074||南京烽火星空通信发展有限公司 南京 210019
- 2. 南京烽火星空通信发展有限公司 南京 210019
- 折叠
摘要
Abstract
The existing weakly-supervised semantic segmentation methods rely on initial response and classification task,on-ly focus on distinguishing target object area,and cannot optimize loss function through complete area.This paper presents a seman-tic segmentation algorithm based on candidate regions for weakly-supervised semantic segmentation of image-level annotation data.In this algorithm,mixed data enhancement scheme is first introduced in the classification network,then the corresponding strategy is formulated to cluster image features,subclass targets are constructed and subclass tags are generated,so that the training process is not only dependent on distinguishable object areas.Comprehensive experiments and analyses are carried out on the PASCAL VOC 2012 dataset,the algorithm shows good performance compared to other weakly supervised semantic segmentation algorithms.By us-ing the method of mixed data enhancement and self-supervised candidate regions generation,the original image produces a more complete response map,which improves the Intersection over Union(IOU)by 2.1%and improves the performance of the final seg-menting network.关键词
弱监督学习/图像语义分割/混合数据增强/候选区域生成Key words
weakly-supervised learning/image semantic segmentation/mixed data enhancement/candidate regions genera-tion分类
信息技术与安全科学引用本文复制引用
王祎,汪洋..基于候选区域生成的弱监督图像语义分割算法[J].计算机与数字工程,2024,52(2):572-577,6.
