Improving Human-Machine Cooperative Visual Search With Soft Highlighting
Improving Human-Machine Cooperative Visual Search With Soft Highlighting
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DOI：
10.1145/3129669
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发表时间：
2017-11-01
影响因子：
1.6
通讯作者：
Mozer, Michael C.
中科院分区：
文献类型：
--
作者：
Kneusel, Ronald T.;Mozer, Michael C.
机器学习的进步已经产生了在某些视觉任务（例如物体识别）上达到人类水平性能的系统。尽管如此，其他需要视觉专业知识的任务在一段时间内不太可能委托给机器，例如卫星和医学图像分析。我们描述了一种人机协作的视觉搜索方法，其目的是超越人类或机器单独行动。使用自动分类器增强人类表现的传统途径是在图像中可能包含目标的区域周围绘制方框。人类专家通常拒绝这种类型的硬突出显示。相反，我们提出了一种软突出显示技术，其中基于分类器置信水平以分级方式调节视野区域的显着性。我们报告了合成图像和自然图像的实验，表明软突出显示的性能协同作用超过了硬突出显示所获得的性能协同作用。
Advances in machine learning have produced systems that attain human-level performance on certain visual tasks, e.g., object identification. Nonetheless, other tasks requiring visual expertise are unlikely to be entrusted to machines for some time, e.g., satellite and medical imagery analysis. We describe a human-machine cooperative approach to visual search, the aim of which is to outperform either human or machine acting alone. The traditional route to augmenting human performance with automatic classifiers is to draw boxes around regions of an image deemed likely to contain a target. Human experts typically reject this type of hard highlighting. We propose instead a soft highlighting technique in which the saliency of regions of the visual field is modulated in a graded fashion based on classifier confidence level. We report on experiments with both synthetic and natural images showing that soft highlighting achieves a performance synergy surpassing that attained by hard highlighting.