ImageNet: A Large-Scale Hierarchical Image Database

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Presentation transcript:

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 youzhou@usc.edu

Dataset in Computer Vision

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 (1998-10) 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.

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

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

Collect Candidate Images For each synset, the queries are the set of WordNet synonyms Accuracy of Internet image search results: 10% For 500-1000 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

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 2008 - April 2010: 11 million images, 15,000+ synsets

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

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

Properties of ImageNet Scale 14,197,122 images, 21841 synsets indexed Hierarchy densely populated semantic hierarchy

Properties of ImageNet Accuracy Diversity

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

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