基于纹元森林和显著性先验的弱监督图像语义分割方法

弱监督语义分割任务常利用训练集中全体图像的超像素及其相似度建立图模型，使用图像级别标记的监督关系进行约束求解。全局建模缺少单幅图像结构信息，同时此类参数方法受到复杂度限制，无法使用大规模的弱监督训练数据。针对以上问题，该文提出一种基于纹元森林和显著性先验的弱监督图像语义分割方法。算法使用弱监督数据和图像显著性训练随机森林分类器用于语义纹元森林特征(Semantic Texton Forest, STF)的提取。测试时，先将图像进行过分割，然后提取超像素语义纹元特征，利用朴素贝叶斯法进行超像素标记的概率估计，最后在条件随机场(CRF)框架下结合图像显著性信息定义了新的能量函数表达式，将图像的标注(labeling)问题转换为能量最小化问题求解。在MSRC-21类数据库上进行了验证，完成了语义分割任务。结果表明，在并未对整个训练集建立图模型的情况下，仅利用单幅图像的显著性信息也可以得到较好的分割结果，同时非参模型有利于规模数据分析。

Weakly Supervised Semantic Segmentation Based on Semantic Texton Forest and Saliency Prior

Most previous weakly supervised semantic segmentation works utilize the labels of the whole training set and thereby need the construction of a relationship graph about image labels. This method lack of structure information in single image and suffer from enormous quantity parameters which result in expensive computation. In this study, a weakly-supervised semantic segmentation algorithm is proposed. Under Conditional Random Field (CRF) framework, an novel energy function expression is developed based on saliency priors as structure context relationship, which avoids the construction of a huge graph in whole training dataset. Specifically, a nonparametric random Semantic Texton Forest (STF) is obtained using weakly supervised training data and images saliency. Then STF feature is extracted from image superpixels and probability estimates of superpixels label is calculated by naive Bayesian method. Finally, a CRF based optimization algorithm is proposed which can efficiency solved by alpha expansion algorithm. Experiments on the MSRC-21 dataset show that the new algorithm outperforms some previous influential weakly-supervised segmentation algorithms with no building graph in whole training set.