多模型集成的弱监督语义分割算法

Multi-model Integrated Weakly Supervised Semantic Segmentation Method

为减小池化操作造成空间信息丢失的影响,提高基于迁移学习的弱监督语义分割算法的性能,提出一种多模型集成的弱监督图像语义分割算法.该算法在迁移学习算法的基础上,利用多尺度图像的高层语义特征和单尺度图像的高中层相结合的卷积特征,分别训练2个差异化的同质型基分割模型,并与原迁移学习训练的分割模型进行加权平均,集成构造最后的分割模型.同时结合预测类别可信度调整语义分割中对应类别像素的可信度,抑制分割图中的假正例区域,提高分割的精度.在VOC2012数据集上进行实验的结果表明,验证集上的平均重叠率为55.3%,测试集上的平均重叠率为56.9%,比原迁移学习算法分别提升6.1%和11.1%,也优于其他以类标为弱监督信息的语义分割算法.

In order to reduce the impact of loss of spatial information generated by pooling operator and improve the performance of transfer learning for weakly-supervised semantic segmentation algorithm with deep convolutional neural network, this paper designs a weakly-supervised image semantic segmentation algorithm based on multi-model ensemble. Based on transfer learning algorithm, the method firstly utilizes the semantic features from last convolutional layer of a multi-scale image and the convolutional features from the middle and deep layers of a single-scale image to respectively train two different homogeneous segmentation models. And then these models are weighted integrating with the original transfer-learning model to get the final segmentation model. In addition, the algorithm combines the confidence of categories to adjust the pixels’ confidence expecting to suppress the false positive regions in the segmented image to improve the accuracy. Finally, the proposed algorithm is tested in challenging VOC2012 dataset. The results show that the mean intersection-over-union of the proposed algorithm is 55.3% on validation dataset and 56.9% on test set, outperforming the original transfer-learning algorithm by 6.1% and 11.1%, respectively. And the method performs favorably against other segmentation methods using weakly-supervised information based on class labels as well.