The Community for Technology Leaders
2013 IEEE Conference on Computer Vision and Pattern Recognition (2009)
Miami, FL, USA
June 20, 2009 to June 25, 2009
ISBN: 978-1-4244-3992-8
pp: 248-255
Jia Deng , Dept. of Comput. Sci., Princeton Univ., Princeton, NJ, USA
R. Socher , Dept. of Comput. Sci., Princeton Univ., Princeton, NJ, USA
Li Fei-Fei , Dept. of Comput. Sci., Princeton Univ., Princeton, NJ, USA
Wei Dong , Dept. of Comput. Sci., Princeton Univ., Princeton, NJ, USA
Kai Li , Dept. of Comput. Sci., Princeton Univ., Princeton, NJ, USA
Li-Jia Li , Dept. of Comput. Sci., Princeton Univ., Princeton, NJ, USA
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 ldquoImageNetrdquo, 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.
INDEX TERMS
computer vision, ImageNet database, large-scale hierarchical image database, Internet, image retrieval, multimedia data, large-scale ontology, wordNet structure, image resolution, subtree
CITATION
Jia Deng, R. Socher, Li Fei-Fei, Wei Dong, Kai Li, Li-Jia Li, "ImageNet: A large-scale hierarchical image database", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 00, no. , pp. 248-255, 2009, doi:10.1109/CVPRW.2009.5206848
83 ms
(Ver 3.3 (11022016))