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ImageNet : A Large-Scale Hierarchical Image Database

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.

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ImageNet : A Large-Scale Hierarchical Image Database

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  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 youzhou@usc.edu

  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 (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.

  4. WordNet • WordNetis 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 WordNethierarchy, 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 WordNetsynonyms • 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

  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 2008- 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, 21841 synsetsindexed • 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

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