Tools for richer crowd source image annotations

Crowd-sourcing tools such as Mechanical Turk are popular for annotation of large scale image data sets. Typically, these annotations consist of bounding boxes or coarse outlines of objects, in order to keep the interface as simple as possible and to respect browser constraints. However, as most browsers now contain functionality to quickly process images and render shapes to the browser through JavaScript, better annotations can feasibly be generated through the browser given an easy-to-use interface. In this paper, we develop a suite of annotation tools for highfidelity object contouring and 3D pose working within the limitation that, to be accessible to most Mechanical Turk users, the tools must be available through browsers with no plug-ins or extra downloads. We show comparative results exploring the annotation accuracy relative to existing annotation tools.

• Type: Journal
• Year: 2012
• Where: WACV
• Authors: Joshua Little, Austin Abrams, Robert Pless
• Keywords: Annotation Tools, Computer Vision, Mechanical Turk, Plug Ins, WACV 2012