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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. Jiewen Lei jiewenle@usc.edu. About the Paper.

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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 Jiewen Lei jiewenle@usc.edu ImageNet

  2. About the Paper • This paper is mainly an introduction to ImageNet. The paper is organized as follows: • shows properties of ImageNet • Compare ImageNet with current related datasets • Constructing ImageNet- describes without concrete steps • ImageNet Applications • mainly focus on the constructing ImageNet. • It mostly relatives to Crawling and PageRank. ImageNet

  3. ImageNet • A dataset - Datasets and Computer Vision • Based on WordNet - Each node is depicted by images • A knowledge ontology - Taxonomy - Partonomy ImageNet

  4. Constructing ImageNet • 2-step process Step 2 : Clean up the candidate Images by humans Step 1 : Collect candidate images Via the Internet ImageNet

  5. Step 1: Collect Candidate Images from the Internet • For each synset, the queries are the set of WordNet synonyms • Accuracy of Internet Image search results: 10 % - For 500-1000 clean images, needs 10K images • Query expansion - Synonyms: German police dog, German shepherd dog - Appending words form ancestors: sheepdog, dog • Multiple Languages - Italian, Dutch, Spanish, Chinese e.g. 德国牧羊犬， pastore tedesco • More engines: Yahoo! , flickr, Google • Parallel downloading ImageNet

  6. Step 2: Clean up the candidate Images by humans • Rely on humans to verify each candidate image collected for a given synset • Amazon Mechanical Turk (AMT) • used for labeling vision data • 300 images: 0.02 dollar • 14,197,122 images: 946 dollars • 10 repetition: 9460 dollars • Jul 2008 -Apr 2010:11 million images • Present the users with a set of candidate images and the definition of the target synset • let users select the best match ones ImageNet

  7. A Task on AMT Workers do annotation on AMT -Multiple annotations for each images Annotation Results - An average of > 97% accuracy ImageNet

  8. Ensure Accuracy • Users Enhancement • Provide wiki and google links for definitions • Make sure workers read the definition - Definition quiz • Allow more feedback. E.g. “unimagable synset” expert opinion ImageNet

  9. Ensure Accuracy • 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 • Quality Control System ImageNet

  10. Quality Control System • randomly sample an initial subset of image to users - Have multiple users independently label same image • obtain a confidence score table, indicating the probability of an image being a good image given the user votes - Different categories requires different levels of consensus • Proceed until a pre-determined confidence score threshold reached ImageNet

  11. Properties of ImageNet • Scale: 12 subtrees,3,2 million images,5247 categories • Hierarchy: densely populated semantic hierarchy, based on WordNet ImageNet

  12. Properties of ImageNet • Accuracy: clean dataset at all level • Diversity: variable appearances, positions, view points, poses, background clutter, occlusions. ImageNet

  13. 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 ImageNet

  14. Pros and Cons • Pros • Crowdsourcing • Benchmarking • Open: Download Original Images, URLs, Features, Object Attributes, API • Cons • Improve algorithm: PageRank • AMT: hierarchical users based on their ability • Only one tag per image ImageNet

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