1. ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei
Dept. of Computer Science, Princeton University, USA
CVPR 2009
You Zhou

2. Dataset in Computer Vision

3. Dataset in Computer Vision
UIUC Cars (2004)
S. Agarwal, A. Awan, D. Roth
CMU/VASC Faces (1998)
H. Rowley, S. Baluja, T. Kanade
FERET Faces (1998)
P. Phillips, H. Wechsler, J. Huang, P. Raus
COIL Objects (1996)
S. Nene, S. Nayar, H. Murase
MNIST digits ( )
Y LeCun & C. Cortes
KTH human action (2004)
I. Leptev & B. Caputo
Sign Language (2008)
P. Buehler, M. Everingham, A. Zisserman
Segmentation (2001)
D. Martin, C. Fowlkes, D. Tal, J. Malik.

4. WordNet
WordNet is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept.
WordNet as an ontology

5. ImageNet
Image database organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images.
Knowledge ontology: Taxonomy, Partonomy

6. Collect Candidate Images
For each synset, the queries are the set of WordNet synonyms
Accuracy of Internet image search results: 10%
For clean images, need 10K images
Query expansion
Synonyms: German shepherd, German police dog, German shepherd dog, Alsatian
Appending words from ancestors: sheepdog, dog
Multiple languages
Italian, Dutch, Spanish, Chinese
More search engines

7. Clean Candidate Images
Rely on humans to verify each candidate image for a given synset
19 years’ work
No graduate students would want to do this project
Amazon Mechanical Turk (AMT)
300 images: 0.02 dollar
14,197,122 images: 946 dollars
10 repetition: 9460 dollars
July April 2010: 11 million images, 15,000+ synsets

8. HIT Design HIT(Human Intelligence Task) Application Qualification Test
Start tasks
Learn about the keyword: Wiki, Google
Definition quiz: choice question about the keyword
Choose images fit the keyword (Yes or No)
Pass cheating detection
Feedback

9. Quality Control System
Human users make mistakes
Not all users follow the instructions
Users do not always agree with each other
Subtle or confusing synsets, e.g. Burmese cat

10. Properties of ImageNet
Scale
14,197,122 images, synsets indexed
Hierarchy
densely populated semantic hierarchy

11. Properties of ImageNet
Accuracy
Diversity

12. ImageNet Applications
Non-parametric Object Recognition
NN-voting + noisy ImageNet
NN-voting + clean ImageNet
Naive Bayesian Nearest Neighbor (NBNN)
NBNN-100
Tree Based Image Classification
Automatic Object Localization

13. Pros and Cons Pros: Large dataset as training resource Benchmarking
Open: Download Original Images, URLs, Features, Object Attributes, API
Cons:
The matching between physical world/ WordNet / ImageNet.
Counterword
Only one tag per image