Type of site | Search engine |
---|---|
Available in | Multilingual (~100) |
Owner | Google, a subsidiary of Alphabet Inc. |
URL | images |
Commercial | Yes |
Registration | Optional |
Launched | July 12, 2001 |
Current status | Active |
Google Images (previously Google Image Search) is a search engine owned by Google 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, reverse image search functionality was added.
When searching for an image, a thumbnail of each matching image is displayed. When the user clicks on a thumbnail, the image is displayed in a larger size, and users may visit the webpage on which the image is used.
In 2000, Google Search results were limited to simple pages of text with links. Google's developers worked on developing this further, and they realized that an image search tool was required to answer "the most popular search query" they had seen to date: the green Versace dress of Jennifer Lopez worn in February 2000. [5] Google paired a recently hired engineer Huican Zhu with product manager Susan Wojcicki (former CEO of YouTube) to build the feature, and they launched Google Image Search in July 2001. [6] That year, 250 million images were indexed in Image Search. This grew to 1 billion images by 2005 and over 10 billion images by 2010. [7]
In January 2007, Google updated the interface for the image search, where information about an image, such as resolution and URL, was hidden until the user moved the mouse cursor over its thumbnail. This was discontinued after a few weeks. [8]
On October 27, 2009, Google Images added a feature to its image search that can be used to find similar images. [9]
On July 20, 2010, Google made another update to the interface of Google Images, which hid image details until mouseover. [10]
In May 2011, Google introduced a sort by subject feature for a visual category scheme overview of a search query. [11]
In June 2011, Google Images added a "Search by Image" feature which allowed for reverse image searches directly in the image search-bar without third-party add-ons. This feature allows users to search for an image by dragging and dropping one onto the search bar, uploading one, or copy-pasting a URL that points to an image into the search bar. [12]
On December 11, 2012, Google Images' search engine algorithm was changed once again, in the hopes of preventing pornographic images from appearing when non-pornographic search terms were used. [13] [14] According to Google, pornographic images would still appear as long as the term searched for was specifically pornographic; otherwise, they would not appear. While Google stated explicitly that they were "not censoring any adult content," it was immediately noted that even when entering terms such as or "Breast," no explicit results were shown. [15] [16] [17] The only alternative option was to turn on an even stricter filter which would refuse to search for the aforementioned terms whatsoever. [17] Users could also no longer exclude keywords from their searches. [18]
On February 15, 2018, the interface was modified to meet the terms of a settlement and licensing partnership with Getty Images. The "View image" button (a deep link to the image itself on its source server) was removed from image thumbnails. This change is intended to discourage users from directly viewing the full-sized image (although doing so using a browser's context menu on the embedded thumbnail is not frustrated), and encourage them to view the image in its appropriate context (which may also include attribution and copyright information) on its respective web page. The "Search by image" button has also been downplayed, as reverse image search can be used to find higher-resolution copies of copyrighted images. Google also agreed to make the copyright disclaimer within the interface more prominent. [19]
On August 6, 2019, the ability to filter images by their image resolutions was removed, as well as "larger than," "face," and "full color" filters. [20]
The relevancy of search results has been examined. Most recently (October 2022), it was shown that 93.1% images of 390 anatomical structures were relevant to the search term. [21]
Google Images has a Search by Image feature for performing reverse image searches. Unlike traditional image retrieval, this feature removes the need to type in keywords and terms into the Google search box. Instead, users search by submitting an image as their query. [12] Results may include similar images, web results, pages with the image, and different resolutions of the image. Images on Google may take anything between 2–30 days to index if they are properly formatted.
The precision of Search by Image's results is higher if the search image is more popular. [22] Additionally, Google Search by Image offers a "best guess for this image" based on the descriptive metadata of the results.
In 2022, the feature was replaced by Google Lens as the default visual search method on Google, and the Search by Image function remains available within Google Lens. [23]
The general steps that Search by Image takes to get from a submitted image to returned search results are as follows: [24]
Google Search is a search engine operated by Google. It allows users to search for information on the Web by entering keywords or phrases. Google Search uses algorithms to analyze and rank websites based on their relevance to the search query. It is the most popular search engine worldwide.
Spamdexing is the deliberate manipulation of search engine indexes. It involves a number of methods, such as link building and repeating related and/or unrelated phrases, to manipulate the relevance or prominence of resources indexed in a manner inconsistent with the purpose of the indexing system.
A metasearch engine is an online information retrieval tool that uses the data of a web search engine to produce its own results. Metasearch engines take input from a user and immediately query search engines for results. Sufficient data is gathered, ranked, and presented to the users.
Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval is opposed to traditional concept-based approaches.
SafeSearch is a feature in Google Search and Google Images, and later, Bing, that acts as an automated filter of pornography and other potentially offensive and inappropriate content.
A search engine is a software system that provides hyperlinks to web pages and other relevant information on the Web in response to a user's query. The user inputs a query within a web browser or a mobile app, and the search results are often a list of hyperlinks, accompanied by textual summaries and images. Users also have the option of limiting the search to a specific type of results, such as images, videos, or news.
A search engine results page (SERP) is a webpage that is displayed by a search engine in response to a query by a user. The main component of a SERP is the listing of results that are returned by the search engine in response to a keyword query.
Bing Videos is a video search service and part of Microsoft's Bing search engine. The service enables users to search and view videos across various websites. Bing Videos was officially released on September 26, 2007 as Live Search Video, and rebranded as Bing Videos on June 1, 2009.
Google and its subsidiary companies, such as YouTube, have removed or omitted information from its services in order to comply with company policies, legal demands, and government censorship laws.
Google Personalized Search is a personalized search feature of Google Search, introduced in 2004. All searches on Google Search are associated with a browser cookie record. When a user performs a search, the search results are not only based on the relevance of each web page to the search term, but also on which websites the user visited through previous search results. This provides a more personalized experience that can increase the relevance of the search results for the particular user. Such filtering may also have side effects, such as the creation of a filter bubble.
Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc.
Image meta search is a type of search engine specialised on finding pictures, images, animations etc. Like the text search, image search is an information retrieval system designed to help to find information on the Internet and it allows the user to look for images etc. using keywords or search phrases and to receive a set of thumbnail images, sorted by relevancy.
Natural-language user interface is a type of computer human interface where linguistic phenomena such as verbs, phrases and clauses act as UI controls for creating, selecting and modifying data in software applications.
Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image or the popularity of an image, and to discover manipulated versions and derivative works.
American entertainer Jennifer Lopez wore a green Versace silk chiffon dress to the 42nd Grammy Awards ceremony on February 23, 2000. The sheer fabric was printed with a tropical leaf and bamboo pattern, and cut with a very low neckline that extended well past Lopez's navel, while the waist of the dress was studded with citrines.
Facebook Graph Search was a semantic search engine that Facebook introduced in March 2013. It was designed to give answers to user natural language queries rather than a list of links. The name refers to the social graph nature of Facebook, which maps the relationships among users. The Graph Search feature combined the big data acquired from its over one billion users and external data into a search engine providing user-specific search results. In a presentation headed by Facebook CEO Mark Zuckerberg, it was announced that the Graph Search algorithm finds information from within a user's network of friends. Microsoft's Bing search engine provided additional results. In July it was made available to all users using the U.S. English version of Facebook. After being made less publicly visible starting December 2014, the original Graph Search was almost entirely deprecated in June 2019.
Contextual search is a form of optimizing web-based search results based on context provided by the user and the computer being used to enter the query. Contextual search services differ from current search engines based on traditional information retrieval that return lists of documents based on their relevance to the query. Rather, contextual search attempts to increase the precision of results based on how valuable they are to individual users.
RankBrain is a machine learning-based search engine algorithm, the use of which was confirmed by Google on 26 October 2015. It helps Google to process search results and provide more relevant search results for users. In a 2015 interview, Google commented that RankBrain was the third most important factor in the ranking algorithm, after with links and content, out of about 200 ranking factors. whose exact functions in the Google algorithm are not fully disclosed. As of 2015, "RankBrain was used for less than 15% of queries." The results show that RankBrain guesses what the other parts of the Google search algorithm will pick as the top result 80% of the time, compared to 70% for human search engineers.
Local search engine optimization is similar to (national) SEO in that it is also a process affecting the visibility of a website or a web page in a web search engine's unpaid results often referred to as "natural", "organic", or "earned" results. In general, the higher ranked on the search results page and more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users; these visitors can then be converted into customers. Local SEO, however, differs in that it is focused on optimizing a business's online presence so that its web pages will be displayed by search engines when users enter local searches for its products or services. Ranking for local search involves a similar process to general SEO but includes some specific elements to rank a business for local search.
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. First announced during Google I/O 2017, it was first provided as a standalone app, later being integrated into Google Camera but was reportedly removed in October 2022. It has also been integrated with the Google Photos and Google Assistant app and with Bard as of 2023.