CBIR

=__CONTENT-BASED INFORMATION RETRIEVAL (CBIR)__=

====By using this CBIR method, it is actually the easiest way to get the information that the users wish to search in the fastest speed without being directed to some unrelated content that the users doesn't need. In other words, the result of the query can be shown with more relevant selections first followed by less relevant selections. With this content based retrieval method, which enable users to search multimedia information in terms of the actual content which include image, audio, video, shapes, textures, or any other information that can be derived from the image itself. In short, it is a system that can filter images based in their content and provide a better indexing and give a more accurate results .====

=__WHY CONTENT-BASED IMAGE RETRIEVAL IS NEEDED__= When we want to search information in search engines such as Google and others search engines,our search results usually end up with any words that we type into those search engines even though those results don't related to our search.As a result, a lot of unnecessary links or pages that are not related to our search were produced.CBIR filter search results based on their contents would help narrowed down our results to more specific ones."Content-based" means that the search will analyze the actual contents of the image rather than metadata that was use in search engines such as keywords,tags,descriptions and others."Content" might refer to colour,shapes,textures or any other information that can be derived from the image itself.Keywords for images manually enter by humans in a large database can be inefficient,expensive and may not capture every keywords describes the images.

=__CONTENT-BASED IMAGE RETRIEVAL__=

====It will store info about the locations of the faces in photos. So, the identities of individual appear in photos are important aspect to the system. Retrieval is performed by comparing features of a query images with corresponding features of image stored in database and representing the user with the images that have the most similar features. If the users is not satisfied with the retrieved result, they can research the result by selecting the most relevant to the search query and get the newest information. Besides, this will also help users with different cultural background and language to effectively search the information the want using this method. This system also cluster the image within a single event based on face arrangement.But, the difficulty in the current computer technology still mainly focus on lower level features, such as shape, text and colour.====



=List of CBIR engines=
 * Name ||~ Description ||~ External Image Query ||~ Metadata Query ||~ Index Size (Estimate, Millions of Images) ||~ Organization Type ||~ License (Open/Closed) ||~  ||
 * [|Bing Image Search] || Microsoft's CBIR engine || No || Yes ||  || Public Company || Closed ||
 * [|Elastic Vision] || Smart image searcher with content-based clustering in a visual network. || No || No ||  || Private Company || Closed ||
 * [|Google Image Search] || Google's CBIR system, note: does not work on all images || No || Yes ||  || Public Company || Closed ||
 * [|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 ||
 * [|vSearch Visual Image Search] || CBIR search engine, by pixolution || Yes || No || 10M || Private Company || Closed ||
 * [|Incogna Image Search] || CBIR search engine, by Incogna Inc. || No || Yes || 100M || Private Company || Closed ||
 * [|Like.com] || Shopping & fashion based CBIR engine || No || Yes || 1M || Private Company || Closed ||
 * [|MiPai similarity search engine] || Online similarity search engine || Yes || Yes || 100M || Individual || Closed ||
 * [|Piximilar] || Demo engine, developed by Idee Inc. || No || No || 3M || Private Company || Closed ||
 * [|Empora] || Product comparison & shopping using CBIR for product images. Previously known as Pixsta || No || Yes || 0.5M || Private Company || Closed ||
 * [|Shopachu] || Shopping & fashion CBIR engine, by Incogna Inc. || No || Yes || 1M || Private Company || Closed ||
 * [|TinEye] || CBIR site for finding variations of web images, by Idee Inc. || Yes || No || 1800M || Private Company || Closed ||
 * [|Tiltomo] || CBIR system using Flickr photos || No || Yes ||  || Private Company || Closed ||
 * [|eBay Image Search] || Image Search for eBay Fashion || No || Yes || 20M || Public Company || Closed ||

CBIR research projects/demos/open source projects

 * ~ Name ||~ Description ||~ External Image Query ||~ Metadata Query ||~ Index Size (Estimate, Millions of Images) ||~ Organization Type ||~ License (Open/Closed) ||
 * [|ALIPR] || Developed by Penn State University researchers || Yes || Yes ||  || University || Closed ||
 * [|Anaktisi] || This Web-Solution implements a new family of CBIR descriptors. These descriptors combine in one histogram color and texture information and are suitable for accurately retrieving images. || Yes || No || 0.225M || University || Open ||
 * [|BRISC] || BRISC is a recursive acronym for BRISC Really IS Cool, and is (conveniently enough) also an anagram of Content-Based Image Retrieval System. || Yes || No ||  || University || GPL ||
 * [|Caliph & Emir] || Creation and Retrieval of images based on MPEG-7. || Yes || No || Desktop-based || University || [|GPL] ||
 * [|CIRES] || developed by the University of Texas at Austin. || Yes || No ||  || University || Closed ||
 * [|FIRE] || Open source query by visual example CBIR system. Developed at RWTH Aachen University. [|FIRE] is a research system developed with extensibility in mind and can easily be combined with textual information retrieval systems. || Yes || No ||  || University || Open ||
 * [|GNU Image Finding Tool] || Query by example image search system. || Yes || No || Desktop-based || GNU || [|GPL] ||
 * [|ISSBP] || Similar Image Search by Imense plugin for Adobe Bridge, free beta. || Yes || Yes || free-beta limited to 4k images || Private Company || Closed ||
 * [|img(Rummager)] || Image retrieval Engine (Freeware Application). || Yes || No || Desktop-based || Individual || Closed ||
 * [|imgSeek] || photo collection manager and viewer with content-based search and many other features. || Yes || No ||  || Individual || [|GPL] ||
 * [|IKONA] || Generic CBIR system - INRIA - IMEDIA || Yes || Yes ||  || University || Closed ||
 * [|MIFile] || Image similarity search engine based on MI File (Metric Inverted File). || Yes || No || 100M || Research Institute || Open ||
 * [|MUVIS] || CBIR System at TUT- Tampere University of Technology. || Yes || No || Desktop-based || University || Closed ||
 * [|PIRIA] || CBIR tool developed at CEA-LIST, LIC2M (Multimedia Multilingual Knowledge Engineering Laboratory). || Yes || Yes || 3M || University || Closed ||
 * [|PicsLikeThat] || Image search using visual similarity search and sorting combined with a recommender system. (Cooperation of pixolution, fotolia and HTW Berlin) || No || No || 12M || University || Closed ||
 * [|Pixcavator] || Similar image search based on topological image analysis || Yes || No || Desktop-based || Private company || Closed ||
 * [|pixolu] || visual & semantic image search engine, by pixolution GmbH & HTW Berlin. || Yes || No ||  || Private Company / University || Closed ||
 * [|RETIN] || Interactive images retrieval system - CNRS - ETIS Lab., MIDI Team || No || No ||  || University || Closed ||
 * [|Retrievr] || Search and explore in a selection of Flickr images by drawing a rough sketch or uploading an image. || No || No ||  || University || Closed ||
 * [|SIMBA] || demo of system by the Albert-Ludwigs-Universitet Freiburg (Germany) Inst. for Pattern Recognition and Image Processing || Yes || No || 0.002M || University || Closed ||
 * [|TagProp] || The demonstration of image annotation tool TagProp in ICCV2009 for image set: Corel 5k ESP Game IAPR TC-12 and MIR Flickr. || No || Yes ||  || Institute || Closed ||
 * [|VIRaL] || Visual Image Retrieval and Localization: A visual search engine that, given a query image, retrieves photos depicting the same object or scene under varying viewpoint or lighting conditions. Using Flickr photos of urban scenes, it automatically estimates where a picture is taken, suggests tags, identifies known landmarks or points of interest, and links to relevant Wikipedia articles. It currently supports 39 cities around the world. || Yes || Yes || 2.221M || University || Closed ||
 * [|Windsurf] || A general framework for efficiently processing content-based image queries with particular emphasis to the region-based paradigm; it provides an environment where different alternatives of the paradigm can be implemented, allowing such implementations to be compared on a fair basis, from the points of view of both effectiveness and efficiency. || Yes || No ||  || University || Closed ||   ||

=__LIMITATIONS OF CONTENT-BASED IMAGE RETRIEVAL__= There is a limited results in current CBIR.People ignore lessons about feature selections and the curse of "dimensionality" in pattern recognition because there is little connection between pixel statistics and the human interpretation of an image(the semantic gap). The use of large numbers of generic features makes highly likely that results will not be scalable,that is they will not hold on collections of images other than the ones used during the development of the method.In other words,the transformation from images to features(or other descriptions) is many to one and when the data set is relatively small,but as the size of the set increase unrelated images are likely to be mapped into the same features.

Reference : http://en.wikipedia.org/wiki/List_of_CBIR_Engines [] encyclopedia of multimedia (Borko Furth) 2006 & 2nd edition 2008 Feb,d,Siu,C,Zhang,H.Eds.(2008).Multimedia information retrieval and management:Technological fundamentals and applications.Berlin:Springer http://www.theopavlidis.com/CBIR/Paper B/vers 3.htm