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Imense Image Search Portal: CBIR search engine, by Imense. No Yes 3M Private Company Closed Imprezzeo Image Search: CBIR search engine, by Imprezzeo. No Yes Private Company Closed Incogna Image Search: CBIR search engine, by Incogna Inc. No Yes 100M Private Company Closed MiPai similarity search engine: Online similarity search engine Yes Yes 100M
General scheme of content-based image retrieval. 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 (see this survey [1] for a scientific overview of the CBIR field).
A visual search engine is a search engine designed to search for information on the World Wide Web through a reverse image search. Information may consist of web pages, locations, other images and other types of documents. This type of search engines is mostly used to search on the mobile Internet through an image of an unknown object (unknown ...
The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web. AOL.
Web search engine supporting natural language queries: Altavista is launched. This is a first among web search engines in many ways: it has unlimited bandwidth, allows natural language queries, has search tips, and allows people to add or delete their domains in 24 hours. [13] [14] 1996 New web search engine
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. [1]
An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words.
The problem of content-based image retrieval is that of improving search results by taking into account visual information contained in the images themselves. Several CBIR methods make use of classifiers trained on image search results, to refine the search. In other words, object categorization from image search is one component of the system.