80 million tiny images: A large data set for nonparametric object and scene recognition

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 nonparametric methods, we explore this world with the aid of a large data set 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 data set are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 nonabstract nouns in English, as listed in the Wordnet lexical database. Hence, the image database gives comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with the 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 data set, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.