2009-imagenet-a-large-scale-hierarchical-image-database.md

title	authors	fieldsOfStudy	meta_key	numCitedBy	reading_status	ref_count	tags	urls	venue	year
ImageNet - A large-scale hierarchical image database	Jia Deng Wei Dong R. Socher Li-Jia Li K. Li Li Fei-Fei	Computer Science	2009-imagenet-a-large-scale-hierarchical-image-database	27839	TBD	27	dataset gen-from-ref paper	https://www.semanticscholar.org/paper/ImageNet:-A-large-scale-hierarchical-image-database-Deng-Dong/d2c733e34d48784a37d717fe43d9e93277a8c53e?sort=total-citations	2009 IEEE Conference on Computer Vision and Pattern Recognition	2009

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ImageNet - A large-scale hierarchical image database

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 large-scale 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.

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