Imagenet: A large-scale hierarchical image database

Imagenet: A large-scale hierarchical image database (2009)

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by Jia Deng , Wei Dong , Richard Socher , Li-jia Li , Kai Li , Li Fei-fei

Venue:	In CVPR
Citations:	109 - 7 self

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Datum	Value	Source
TITLE	Imagenet: A large-scale hierarchical image database	INFERENCE
AUTHOR NAME	Jia Deng	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science, Princeton University, USA	SVM HeaderParse 0.2
AUTHOR NAME	Wei Dong	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science, Princeton University, USA	SVM HeaderParse 0.2
AUTHOR NAME	Richard Socher	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science, Princeton University, USA	SVM HeaderParse 0.2
AUTHOR NAME	Li-jia Li	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science, Princeton University, USA	SVM HeaderParse 0.2
AUTHOR NAME	Kai Li	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science, Princeton University, USA	SVM HeaderParse 0.2
AUTHOR NAME	Li Fei-fei	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science, Princeton University, USA	SVM HeaderParse 0.2
ABSTRACT	The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a largescale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond. 1.	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE	In CVPR	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	27 found	ParsCit 1.0