Imagenet: A large-scale hierarchical image database

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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:	839 - 28 self

BibTeX

@INPROCEEDINGS{Deng09imagenet:a,
    author = {Jia Deng and Wei Dong and Richard Socher and Li-jia Li and Kai Li and Li Fei-fei},
    title = {Imagenet: A large-scale hierarchical image database},
    booktitle = {In CVPR},
    year = {2009}
}

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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.

Keyphrases

large-scale hierarchical image database critical problem wordnet structure image data semantic hierarchy image classification large-scale database computer vision community multimedia data largescale ontology amazon mechanical turk data collection scheme new database unparalleled opportunity full resolution image object recognition automatic object annotated image current state simple application detailed analysis robust model challenging task hierarchical structure current image datasets 500-1000 clean

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