I-Corps: Combining Machine Vision and Crowdsourcing for Convenient and Accurate Image Annotation

快速、准确和廉价地注释大量图像将是科学、医学和其他商业应用中的宝贵能力。 机器视觉正在这些领域取得进展。 然而，对于许多应用来说，精度还不够。 近年来，一种补充解决方案已经出现：众包，即动态招募数千人从他们的计算机上执行指定的任务。 该团队的研究表明，有可能将人类注释者和机器的互补优势联合收割机结合到一个灵活、准确、快速和廉价的混合系统中。 为了证明其有效性和潜在的商业机会，该团队将围绕这种方法开发原型和商业模式。随着成像变得越来越可用，存储成本越来越低，图像数据量将继续增加。 科学、研究、地理空间信息系统和消费者市场都是如此。 拟议的努力将解决对这些数据进行规模化注释和分析的需求，同时利用当今的计算能力保持尽可能廉价和快速的过程。 通过将计算机视觉和机器学习自动化与人类（专家和非专家注释者）相结合，该系统有望在几乎任何图像分析挑战中快速配置和训练。

Annotating a large body of images quickly, accurately and inexpensively would be a valuable capability in scientific, medical and other commercial applications. Machine vision is making progress in these arenas. However, accuracy is not yet sufficient for many applications. In recent years, a complementary solution has become available: crowdsourcing, that is, dynamically recruiting thousands of people to carry out an assigned task from their computer. The team's research suggests that it is possible to combine the complementary strengths of human annotators and machines into a hybrid system that is flexible, accurate, fast and inexpensive. To demonstrate effectiveness and potential commercial opportunity, the team will develop a prototype and a business model around this approach.As imaging becomes more available and storage inexpensive, the amount of image data will continue to increase. This is true for the scientific, research, geospatial information systems and consumer markets. The proposed effort will address the need to scale annotation and analysis of this data while keeping the process as inexpensive and fast as possible with today's computational power. By combining computer vision and machine learning automations with humans (both experts and non-expert annotators), the system promises to be quickly configurable and trainable across virtually any image analysis challenge.