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Similar Images - find the right image - fast

Similar Images - find the right image - fast
Related:  Content Based Image Retrieval

imgSeek How to Search Images Online –Search Engine Journal Image search is evolving rapidly. Today the machine understands much more about images than just a year ago: it can read the text on the image, see its colors and classify it based on its form, shape and textures. So which advanced image search methods can we use today? Image search based on image content (face / landscape / photo / product image search) Face search has been a hot topic recently. Exalead has become known for integrating facial recognition technology but it still lacks some accuracy. Image search based on color Picitup allows to set color preferences (choose among 18 colors to set the search dominating palette);PicSearch and Snap.com recognize between colorful and black-and-white images.Etsy searches only inside its own product database but its color-based search engine is both fun and pleasure to play with. Image search based on similarity Recently launched Tineye.com (registration required) searches for similar images online.

GazoPa similar image search imense® TinEye image search reverse TinEye is a reverse image search engine. TinEye is a reverse image search engine. It finds out where an image came from, how it is being used, if modified versions of the image exist, or if there is a higher resolution version. TinEye TinEye is a reverse image search engine. TinEye is the first image search engine on the web to use image identification technology rather than keywords, metadata or watermarks. TinEye regularly crawls the web for new images, and we also accept contributions of complete online image collections. Company Profile TinEye is brought to you by the good folks at Idée Inc., an advanced image recognition and search software company. PixID – Editorial image monitoring for the news and entertainment photo industry. Idée is an independent, privately held company headquartered in Toronto, Canada and we are hiring. TinEye Contributors Our goal with TinEye is to connect images and information and to make sure that images can be attributed to their creator.

Image retrieval The first microcomputer-based image database retrieval system was developed at MIT, in the 1990s, by Banireddy Prasaad, Amar Gupta, Hoo-min Toong, and Stuart Madnick.[1] A 2008 survey article documented progresses after 2007.[2] Search methods[edit] Image search is a specialized data search used to find images. Image meta search - search of images based on associated metadata such as keywords, text, etc.Content-based image retrieval (CBIR) – the application of computer vision to the image retrieval. Data Scope[edit] It is crucial to understand the scope and nature of image data in order to determine the complexity of image search system design. Archives - usually contain large volumes of structured or semi-structured homogeneous data pertaining to specific topics.Domain-Specific Collection - this is a homogeneous collection providing access to controlled users with very specific objectives. Evaluations[edit] See also[edit] References[edit] Jump up ^ Prasad, B E; A Gupta, H-M Toong, S.E.

Image Recognition and Visual Search

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