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.

Keywords

Computer scienceWordNetHierarchyCluster analysisArtificial intelligenceInformation retrievalScale (ratio)Object (grammar)Image (mathematics)OntologyData miningMachine learningPattern recognition (psychology)

Affiliated Institutions

Related Publications

The Generalized A* Architecture

We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in ...

2007 Journal of Artificial Intelligence Re... 20 citations

Publication Info

Year
2009
Type
article
Pages
248-255
Citations
58773
Access
Closed

External Links

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

58773
OpenAlex

Cite This

Jia Deng, Wei Dong, Richard Socher et al. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition , 248-255. https://doi.org/10.1109/cvpr.2009.5206848

Identifiers

DOI
10.1109/cvpr.2009.5206848