title: Surpassing Humans and Computers with JellyBean: Crowd-Vision-Hybrid Counting Algorithms.
abstract: Counting objects is a fundamental image processisng primitive, and has many scientific, health, surveillance, security, and military applications. Existing supervised computer vision techniques typically require large quantities of labeled training data, and even with that, fail to return accurate results in all but the most stylized settings. Using vanilla crowd-sourcing, on the other hand, can lead to significant errors, especially on images with many objects. In this paper, we present our JellyBean suite of algorithms, that combines the best of crowds and computer vision to count objects in images, and uses judicious decomposition of images to greatly improve accuracy at low cost. Our algorithms have several desirable properties:(i)they aretheoretically optimal or near-optimal, in that they ask as few questions as possible to humans (under certain intuitively reasonable assumptions that we justify in our paper experimentally);(ii)they operate understand-aloneorhybridmodes, in that they can either work independent of computer vision algorithms, or work in concert with them, depending on whether the computer vision techniques are available or useful for the given setting;(iii)theyperform very wellin practice, returning accurate counts on images that no individual worker or computer vision algorithm can count correctly, while not incurring a high cost.
authors: Stanford University.
authors: Akash Das
authors: AD
authors: Sarma
authors: University of Illinois.
authors: Ayush
authors: A
authors: Jain
authors: The Ohio State University.
authors: Arnab
authors: A
authors: Nandi
authors: University of Illinois.
authors: Aditya
authors: A
authors: Parameswaran
authors: Stanford University.
authors: Jennifer
authors: J
authors: Widom