融合多尺度信息的弱监督语义分割及优化

熊昌镇; 智慧

doi:10.11959/j.issn.1000-436x.2019004

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融合多尺度信息的弱监督语义分割及优化

学术通信 | 更新时间：2024-06-05

- 融合多尺度信息的弱监督语义分割及优化
- Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model
- 通信学报 2019年40卷第1期页码：163-171
- 作者机构：
  
  北方工业大学城市道路交通智能控制技术北京市重点实验室，北京 100144
- 作者简介：
  
  [ "熊昌镇（1979-），男，福建建宁人，博士，北方工业大学副教授，主要研究方向为交通图像处理、机器学习。" ]
  [ "智慧（1991-），女，内蒙古乌兰察布人，北方工业大学硕士生，主要研究方向为图像语义分割、注意力机制。" ]
- 基金信息：
  
  国家重点研究发展计划基金资助项目(2017YFC0821102)
- DOI：10.11959/j.issn.1000-436x.2019004
  中图分类号： TP18;TP391.4
- 网络出版日期：2019-01，
  
  纸质出版日期：2019-01-25
- 稿件说明：
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熊昌镇, 智慧. 融合多尺度信息的弱监督语义分割及优化[J]. 通信学报, 2019,40(1):163-171.

Changzhen XIONG, Hui ZHI. Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model[J]. Journal on communications, 2019, 40(1): 163-171.
熊昌镇, 智慧. 融合多尺度信息的弱监督语义分割及优化[J]. 通信学报, 2019,40(1):163-171. DOI： 10.11959/j.issn.1000-436x.2019004.

Changzhen XIONG, Hui ZHI. Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model[J]. Journal on communications, 2019, 40(1): 163-171. DOI： 10.11959/j.issn.1000-436x.2019004.

摘要

为提高弱监督语义分割算法精度，提出一种融合多尺度特征的分割及优化算法。首先，基于迁移学习算法构建多尺度特征模型，类别预测时引入新分类器，减少因预测目标类信息错误导致分割失败的情况；其次，将多尺度模型与原迁移学习模型进行加权集成，增强模型泛化性能；最后，结合预测类可信度调整分割图中相应类像素的可信度，规避假正例分割区域。在VOC 2012验证集上的平均交并比为58.8%，测试集上的平均交并比为57.5%，同比原迁移学习模型分别提升12.9%和12.3%，也优于其他以类标作为监督信息的语义分割算法。

Abstract

In order to improve the accuracy of weakly-supervised semantic segmentation method

a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition

a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally

the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map

avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset

the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well.

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