Content-based image retrieval General scheme of content-based image retrieval Content-based image retrieval (CBIR), also known as query by image content (QBIC) 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 recent scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see concept-based image indexing). History[edit] The term "content-based image retrieval" seems to have originated in 1992 when it was used by T. The earliest commercial CBIR system was developed by IBM and was called QBIC (Query by Image Content).[3] Technical progress[edit] The interest in CBIR has grown because of the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval. CBIR techniques[edit] Query techniques[edit] Color[edit] Shape[edit]
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.
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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. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images "similar" to the query. 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. Evaluations[edit] There are evaluation workshops for image retrieval systems aiming to investigate and improve the performance of such systems. See also[edit] References[edit]
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