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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 original paper (2004) reported that a pair of players can produce 3.89 ± 0.69 labels per minute. At this rate, 5,000 people continuously playing the game could provide one label per image indexed by Google (425 million) in 31 days. [1] In late 2008, the game was rebranded as GWAP ("game with a purpose"), with a new user interface.
image, label 2008 [3] Torralba et al. Street View House Numbers (SVHN) 630,420 digits with bounding boxes in house numbers captured in Google Street View. 630,420 image, label, bounding boxes 2011 [4] [5] Netzer et al. JFT-300M Dataset internal to Google Research. 303M images with 375M labels in 18291 categories 303,000,000 image, label 2017 [6 ...
Crowdsource includes a variety of short tasks users can complete to improve many of Google's different services. Such tasks include image label verification, sentiment evaluation, and translation validation. By completing these tasks, users provide Google with data to improve services such as Google Maps, Google Translate, and Android. [3]
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 ...
Manual image annotation is the process of manually defining regions in an image and creating a textual description of those regions. Such annotations can for instance be used to train machine learning algorithms for computer vision applications. This is a list of computer software which can be used for manual annotation of images.
Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation.
Google Lens is an image recognition technology developed by Google, designed to bring up relevant information related to objects it identifies using visual analysis based on a neural network. [2] First announced during Google I/O 2017, [ 3 ] it was first provided as a standalone app, later being integrated into Google Camera but was reportedly ...