Human Computation vs. Machine Learning: an Experimental Comparison for Image Classification

Gloria Re Calegari, Gioele Nasi, I. Celino
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引用次数: 9

Abstract

Image classification is a classical task heavily studied in computer vision and widely required in many concrete scientific and industrial scenarios. Is it better to rely on human eyes, thus asking people to classify pictures, or to train a machine learning system to automatically solve the task? The answer largely depends on the specific case and the required accuracy: humans may be more reliable - especially if they are domain experts - but automatic processing can be cheaper, even if less capable to demonstrate an "intelligent" behaviour.In this paper, we present an experimental comparison of different Human Computation and Machine Learning approaches to solve the same image classification task on a set of pictures used in light pollution research. We illustrate the adopted methods and the obtained results and we compare and contrast them in order to come up with a long term combined strategy to address the specific issue at scale: while it is hard to ensure a long-term engagement of users to exclusively rely on the Human Computation approach, the human classification is indispensable to overcome the "cold start" problem of automated data modelling.
人类计算与机器学习:图像分类的实验比较
图像分类是计算机视觉领域的一项经典任务,在许多具体的科学和工业场景中都有广泛的应用。是依靠人眼来对图片进行分类,还是训练一个机器学习系统来自动解决这个任务?答案在很大程度上取决于具体情况和所需的准确性:人类可能更可靠——尤其是如果他们是领域专家的话——但自动处理可能更便宜,即使无法证明一种“智能”行为。在本文中,我们对不同的人类计算和机器学习方法进行了实验比较,以解决光污染研究中使用的一组图像的相同图像分类任务。我们对采用的方法和获得的结果进行了说明,并对它们进行了比较和对比,以便提出一个长期的组合策略来大规模解决具体问题:虽然很难确保用户的长期参与完全依赖于人工计算方法,但人工分类对于克服自动数据建模的“冷启动”问题是必不可少的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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