Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
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DOI：
10.1007/s11263-019-01172-6
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发表时间：
2019-09-01
影响因子：
19.5
通讯作者：
Grauman, Kristen
中科院分区：
文献类型：
--
作者：
Gurari, Danna;Zhao, Yinan;Grauman, Kristen
前景目标分割是许多图像分析任务的关键步骤。虽然自动化方法可以产生高质量的结果，但它们的失败使需要实际解决方案的用户失望。我们提出了一个资源分配框架，用于预测如何最好地分配人类注释工作的固定预算，以便为给定的一批图像和自动化方法收集更高质量的分割。该框架基于一个预测模块，该模块估计给定算法绘制的分割的质量。我们展示了该框架在两个新任务中的价值，这些任务与预测如何在算法和人类之间分配注释工作有关。具体来说，我们开发了两个系统，它们自动决定，对于一批图像，何时招募人类和计算机来创建(1)初始化分割工具所需的粗分割和(2)最终的细粒度分割。实验证明，依靠混合的人力和计算机的努力，而不是依靠任何一个资源单独分割的图像对象来自三种不同的模式（可见，相对比显微镜，荧光显微镜）的优势。
Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need of practical solutions. We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods. The framework is based on a prediction module that estimates the quality of given algorithm-drawn segmentations. We demonstrate the value of the framework for two novel tasks related to predicting how to distribute annotation efforts between algorithms and humans. Specifically, we develop two systems that automatically decide, for a batch of images, when to recruit humans versus computers to create (1) coarse segmentations required to initialize segmentation tools and (2) final, fine-grained segmentations. Experiments demonstrate the advantage of relying on a mix of human and computer efforts over relying on either resource alone for segmenting objects in images coming from three diverse modalities (visible, phase contrast microscopy, and fluorescence microscopy).