论文标题
以人为本的无监督分割融合
Human-Centered Unsupervised Segmentation Fusion
论文作者
论文摘要
分割通常是一个问题不足的问题，因为它导致多种解决方案，因此很难定义地面真相数据以评估算法。每个图像只使用一个注释者可以天真地超越问题，但是这种习得并不能代表大多数人对图像的认知感知。如今，通过众包获得多个分割并不难，因此唯一的问题是如何根据图像获得一个地面真相分割。已经存在许多算法解决方案，但是大多数方法都是监督或不考虑人类分割的信心。在本文中，我们介绍了基于K-Modes聚类的新分割融合模型。从具有人类基础真理细分的公开数据集获得的结果清楚地表明，我们的模型的表现优于人类细分的最先进。
Segmentation is generally an ill-posed problem since it results in multiple solutions and is, therefore, hard to define ground truth data to evaluate algorithms. The problem can be naively surpassed by using only one annotator per image, but such acquisition doesn't represent the cognitive perception of an image by the majority of people. Nowadays, it is not difficult to obtain multiple segmentations with crowdsourcing, so the only problem that stays is how to get one ground truth segmentation per image. There already exist numerous algorithmic solutions, but most methods are supervised or don't consider confidence per human segmentation. In this paper, we introduce a new segmentation fusion model that is based on K-Modes clustering. Results obtained from publicly available datasets with human ground truth segmentations clearly show that our model outperforms the state-of-the-art on human segmentations.
