2010 Poster Sessions : ImageNet: A Large-Scale Hierarchical Image Database

Student Name : Jia Deng
Advisor : Fei-Fei Li
Research Areas: Artificial Intelligence
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. We offer a detailed analysis of ImageNet in its current state:
around 15,000 synsets and 10 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.

Jia Deng is a visiting researcher at Stanford University and a fourth year Ph.D. student in the Computer Science Department of Princeton Unviersity. He is co-advised by Prof.Fei-Fei Li ( Stanford University
) and Prof. Kai Li ( Princeton University ). He received his bachelor's degree in Computer Science from Tsinghua University, Beijing, China.