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Google Images (previously Google Image Search) is a search engine owned by Gsuite that allows users to search the World Wide Web for images. [1] It was introduced on July 12, 2001, due to a demand for pictures of the green Versace dress of Jennifer Lopez worn in February 2000. [2] [3] [4] In 2011, Gsuite image search functionality was added.
A standard test image is a digital image file used across different institutions to test image processing and image compression algorithms. By using the same standard test images, different labs are able to compare results, both visually and quantitatively. The images are in many cases chosen to represent natural or typical images that a class ...
The images were scraped from online image search (Google, Picsearch, MSN, Yahoo, Flickr, etc) using synonyms in multiple languages. For example: German shepherd, German police dog, German shepherd dog, Alsatian, ovejero alemán, pastore tedesco, 德国牧羊犬. [22] ImageNet consists of images in RGB format with varying resolutions. For ...
The Google Image Labeler relied on humans that tag the meaning or content of the image, rather than its context looking on at where the image was used. By storing more information about the image, Google stores more possible avenues of discovering the image in response to a user's search.
The Ninth Circuit did, however, overturn the district court's decision that Google's thumbnail images were unauthorized and infringing copies of Perfect 10's original images. Google claimed that these images constituted fair use, and the circuit court agreed. This was because they were "highly transformative." The court did not define what size ...
Google Labs – test and demonstrate new Google products. Discontinued in July. Image Swirl – an enhancement for an image-search tool in Google Labs. It was built on top of image search by grouping images with similar visual and semantic qualities. Shut down in July due to discontinuation of Google Labs. Google Sets – generates a list of ...
The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
Text-to-image models are trained on large datasets of (text, image) pairs, often scraped from the web. With their 2022 Imagen model, Google Brain reported positive results from using a large language model trained separately on a text-only corpus (with its weights subsequently frozen), a departure from the theretofore standard approach. [18]