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

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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.