Papperoni

2008

80 Million Tiny Images: A Large Dataset for Non-Parametric Object and Scene Recognition

Antonio Torralba, Rob Fergus, W. Freeman

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citations

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61

AI summary

This paper introduces a large dataset of 80 million tiny images (32x32 pixels) collected from the web, loosely labeled with Wordnet nouns. It explores non-parametric object and scene recognition using nearest-neighbor methods, achieving comparable performance to class-specific detectors for prevalent classes.

Main Contributions

Introduces a dataset of 79,302,017 tiny images (32x32 pixels) collected from the web and labeled with Wordnet nouns.
Demonstrates that Internet-sized datasets yield a reasonable density over the manifold of natural images for object recognition.
Shows that simple non-parametric methods can give reasonable performance on object recognition tasks when combined with large datasets.
Achieves performance comparable to leading class-specific detectors for richly represented classes like people.
Presents a voting scheme based on the Wordnet semantic hierarchy to cope with labeling noise.

Abstract

With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Web. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 × 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.

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References [43]

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It's a fun read, but in the end is just an application of the spatial pyramid matching kernel from the other paper.

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on November 25, 2025

The initial part about data collection and dataset description was nice, but the part of classifying was a bit overkill

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