论文标题
EDC3：使用特定于类的copula函数来改善语义图像分割的深层分类器的合奏
EDC3: Ensemble of Deep-Classifiers using Class-specific Copula functions to Improve Semantic Image Segmentation
论文作者
论文摘要
在文献中，许多融合技术已注册以进行图像的分割，但它们主要集中于观察到的输出分数的输出或信念得分或概率分数。在目前的工作中，我们利用了不同分类器之间的源统计依赖性，以结合图像的语义分割不同的深度学习技术。为此，在目前的工作中，新提出了一种基于班级的结合方法来解决多级分割问题。在实验上，观察到，使用拟议的类别副副函数的语义图像分割的性能比传统使用的单个copula函数来提高语义图像分割。该性能还与三种最先进的结合方法进行了比较。
In the literature, many fusion techniques are registered for the segmentation of images, but they primarily focus on observed output or belief score or probability score of the output classes. In the present work, we have utilized inter source statistical dependency among different classifiers for ensembling of different deep learning techniques for semantic segmentation of images. For this purpose, in the present work, a class-wise Copula-based ensembling method is newly proposed for solving the multi-class segmentation problem. Experimentally, it is observed that the performance has improved more for semantic image segmentation using the proposed class-specific Copula function than the traditionally used single Copula function for the problem. The performance is also compared with three state-of-the-art ensembling methods.
