"Introduction
The digital era has brought with it an enormous explosion of data. The latest estimations put a number of more than 3 billion photos on Flickr, a similar number of video clips on YouTube and an even larger number for images in the Google Image Search database. More sophisticated and robust models and algorithms can be proposed by exploiting these images, resulting in better applications for users to index, retrieve, organize and interact with these data. But exactly how such data can be utilized and organized is a problem yet to be solved. In this paper, we introduce a new image database called “ImageNet”, a large-scale ontology of images. We believe that a large-scale ontology of images is a critical resource for developing advanced, large-scale content-based image search and image understanding algorithms, as well as for providing critical training and benchmarking data for such algorithms.

ImageNet uses the hierarchical structure of WordNet [9]. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a “synonym set” or “synset”, There are around 80, 000 noun synsets in WordNet. In ImageNet, we aim to provide on average 500–1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated as described in Sec. 3.2. ImageNet, therefore, will offer tens of millions of cleanly sorted images. In this paper, we report the current version of ImageNet, consisting of 12 “subtrees”: mammal, bird, fish, reptile, amphibian, vehicle, furniture, musical instrument, geological formation, tool, flower, fruit. These subtrees contain 5247 synsets and 3.2 million images. Fig. 1 shows a snapshot of two branches of the mammal and vehicle subtrees. The database is publicly available at http://www.image-net.org.

Figure 1: - A snapshot of two root-to-leaf branches of ImageNet: the top row is from the mammal subtree; the bottom row is from the vehicle subtree. For each synset, 9 randomly sampled images are presented.
Figure 1: A snapshot of two root-to-leaf branches of ImageNet: the top row is from the mammal subtree; the bottom row is from the vehicle subtree. For each synset, 9 randomly sampled images are presented.
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The rest of the paper is organized as follows: We first show that ImageNet is a large-scale, accurate and diverse image database (Section 2). In Section 4, we present a few simple application examples by exploiting the current ImageNet, mostly the mammal and vehicle subtrees. Our goal is to show that ImageNet can serve as a useful resource for visual recognition applications such as object recognition, image classification and object localization. In addition, the construction of such a large-scale and high-quality database can no longer rely on traditional data collection methods. Sec. 3 describes how ImageNet is constructed by leveraging Amazon Mechanical Turk.

SECTION 2.Properties of ImageNet
ImageNet is built upon the hierarchical structure provided by WordNet. In its completion, ImageNet aims to contain in the order of 50 million cleanly labeled full resolution images (500–1000 per synset). At the time this paper is written, ImageNet consists of 12 subtrees. Most analysis will be based on the mammal and vehicle subtrees.

Scale
ImageNet aims to provide the most comprehensive and diverse coverage of the image world. The current 12 subtrees consist of a total of 3.2 million cleanly annotated images spread over 5247 categories (Fig. 2). On average over 600 images are collected for each synset. Fig. 2 shows the distributions of the number of images per synset for the current ImageNet . To our knowledge this is already the largest clean image dataset available to the vision research community, in terms of the total number of images, number of images per category as well as the number of categories

Figure 2: - Scale of ImageNet. Red curve: Histogram of number of images per synset. About 20% of the synsets have very few images. Over 50% synsets have more than 500 images. Table: Summary of selected subtrees. For complete and up-to-date statistics visit http://www.image-net.org/about-stats."
From the above paper, summarize and explain what is imagenet. Mention the figures. Try to use your own words, do not copy the sentences directly from the above paper.