Assigning meaning to parts of image data is the goal of semantic image segmentation. Machine learning methods, specifically supervised learning is commonly used in a variety of tasks formulated as semantic segmentation. One of the major challenges in the supervised learning approaches is expressing and collecting the rich knowledge that experts have with respect to the meaning present in the image data. Towards this, typically a fixed set of labels is specified and experts are tasked with annotating the pixels, patches or segments in the images with the given labels. In general, however, the set of classes does not fully capture the rich semantic information present in the images. For example, in medical imaging such as histology images, the different parts of cells could be grouped and sub-grouped based on the expertise of the pathologist. To achieve such a precise semantic representation of the concepts in the image, we need access to the full depth of knowledge of the annotator. In this work, we develop a novel approach to collect segmentation annotations from experts based on psychometric testing. Our method consists of the psychometric testing procedure, active query selection, query enhancement, and a deep metric learning model to achieve a patch-level image embedding that allows for semantic segmentation of images. We show the merits of our method with evaluation on the synthetically generated image, aerial image and histology image.
翻译:图像数据的某些部分的指定含义是语义图像分割的目标。 机器学习方法, 特别监督的学习方法, 通常用于作为语义分割的各种任务中。 监督的学习方法的主要挑战之一是表达和收集专家对图像数据的含义所具备的丰富知识。 为此, 通常要指定一套固定的标签, 专家的任务是用给定标签来说明图像中的像素、 补丁或部分。 但是, 一般来说, 一组班级并不完全捕捉图像中存在的丰富的语义信息。 例如, 在像学图像这样的医学成像中, 细胞的不同部分可以根据病理学家的专业知识进行分组和分组。 要对图像中的概念进行精确的语义描述, 我们需要获得该标记师全方位的知识。 在这项工作中, 我们开发了一种新的方法, 收集专家基于心理测量测试的分解图解图解图。 我们的方法包括心理测试程序、 积极的问答选择、 查询方法 和 图像的精度分析方法, 使得我们图像的合成分解和深度图像生成方法能够实现模型的模型的精度。