Tiny Images Dataset

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 Internet. 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 x 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

随着互联网的兴起，数十亿张图像如今在网络上自由流通，构成了对视觉世界的高密度抽样。借助从互联网收集的包含79,302,017张图像的大型数据集，我们运用多种非参数方法对这一世界进行探索。受心理物理学研究结果启发，该结果表明人类视觉系统对图像分辨率退化的容忍度极高，数据集中的图像以32 x 32彩色图像的形式存储。每张图像都附带了来自Wordnet词汇数据库中75,062个非抽象名词之一的松散标签。因此，该图像数据库全面覆盖了所有物体类别和场景。Wordnet中的语义信息可与最近邻方法相结合，以实现跨越多个语义级别的物体分类，从而最小化标签噪声的影响。对于数据集中特别普遍的某些类别，例如人物，我们能够