Title: 80 million tiny images: a large dataset for non-parametric object and scene
recognition
Abstract:
Billions of images are available online, constituting a dense sampling of the visual world.
In contrast, the existing image datasets range from 102 to 104 images spreading over a
few different classes. Faced to this fact, they collect 79,302,017 images from seven
independent image search engines, loosely labeling one word to each image with 75,062
non-abstract nouns in English as listed in the Wordnet lexical database. Since the low
resolution images still have a good tolerant in object recognition, scene recognition and
segmentation, they store images with resolution of 32 × 32.
Combined with the semantic information from Wordnet and nearest-neighbor methods,
they propose a wordnet voting scheme to solve the semantic gap between images and
semantic meaning. It has a good performance in object recognition and outperforms some
prevalent algorithms.
References:
[1] A. Torralba, R. Fergus and W. T. Freeman. 80 million tiny images: a large dataset for
non-parametric object and scene recognition. Submitted to IEEE Transactions on
Pattern Analysis and Machine Intelligence.
[2] C. Fellbaum. Wordnet: An Electronic Lexical Database. Bradford Books, 1998