LabelMe: A Database and Web-Based Tool for Image Annotation

LabelMe: A Database and Web-Based Tool for Image Annotation (2008)

Cached

Download Links

by B. C. Russell , A. Torralba , K. P. Murphy , W. T. Freeman

Citations:

231 - 36 self

Versions

Version History

Metadata Version 1

User correction supplied by mph

Datum	Value	Source
TITLE	LabelMe: A Database and Web-Based Tool for Image Annotation	user correction
AUTHOR NAME	B. C. Russell	user correction
AUTHOR AFFIL	Computer Science and Artificial Intelligence Laboratory,; Massachusetts Institute of Technology, Cambridge, MA 02139,	user correction
AUTHOR ADDR	USA	user correction
AUTHOR NAME	A. Torralba	user correction
AUTHOR AFFIL	Computer Science and Artificial Intelligence Laboratory,; Massachusetts Institute of Technology, Cambridge, MA 02139,	user correction
AUTHOR ADDR	USA	user correction
AUTHOR NAME	K. P. Murphy	user correction
AUTHOR AFFIL	Departments of computer science and statistics, University of; British Columbia, Vancouver	user correction
AUTHOR ADDR	BC V6T 1Z4, Canada	user correction
AUTHOR NAME	W. T. Freeman	user correction
AUTHOR AFFIL	Computer Science and Artificial Intelligence Laboratory,; Massachusetts Institute of Technology, Cambridge, MA 02139,	user correction
AUTHOR ADDR	USA	user correction
ABSTRACT	We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web.	user correction
YEAR	2008	user correction
CITATIONS	43 found	ParsCit 1.0