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| | Content-based image retrieval - Wikipedia, the free encyclopedia |
 | | 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 to the image retrieval problem, that is, the problem of searching for digital images in large databases. | | | CBIR systems can also make use of relevance feedback, where the user progressively refines the search results by marking images in the results as "relevant", "not relevant", or "neutral" to the search query, then repeating the search with the new information. | | | A content-based image retrieval system (CBIRS) is a piece of software that implements CBIR. |
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http://en.wikipedia.org/wiki/CBIR
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| | [No title] |
| | CBIR draws many of its methods from the field of image processing and computer vision, and is regarded by some as a subset of that field. | | | CBIR in context Although university researchers may experiment with standalone image retrieval systems to test the effectiveness of search algorithms, this is not at all typical of the way they are likely to be used in practice. | | | Primitive features characterizing image content, such as colour, texture, and shape, are computed for both stored and query images, and used to identify (say) the 20 stored images most closely matching the query. |
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http://www.jisc.ac.uk/uploaded_documents/jtap-039.doc
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| | Content-Based Image Retrieval for Medical Databases |
| | An example of a retrieval query of this sort would be ``show me images from a given database that are similar to a particular image.'' A key element of this approach revolves around the types of patterns that can be recognized by the computer and that can serve as the indices of the data retrieval. | | | A content-based image retrieval system is being developed that takes a human-in-the-loop approach, because completely automated approaches are not feasible for radiological images in which the clinically useful information may consist of gray level and texture deviations in highly localized but difficult-to-segment regions of an image. | | | CBIR differs fundamentally in its focus from a variety of pattern recognition and artificial intelligence techniques that have been applied to computer-aided medical image interpretation. |
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http://oldwww.cs.pitt.edu/idm98/Imported/brodley.html
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| | Content-based image retrieval |
| | His main research interests are in the fundamentals of image retrieval by content, theoretical foundation of geometric and photometric invariants, and color in image processing and computer vision. | | | He is guest editor of the special issue on content-based image retrieval for the International Journal of Computer Vision, IJCV, and the special issue on Colour for Image Indexing and Retrieval for the journal of Computer Vision and Image Understanding, CVIU. | | | His current research interest is in computer vision from first principles, texture and material perception, image retrieval and learning object segmentation and visual concepts rather than modelling it and the language - pictorial barrier. |
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http://www.ee.surrey.ac.uk/icpr2004/tutorials/Content-basedimageretrieval_000.htm
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| | Using Structure in Content-based Image Retrieval |
| | In this paper we present a study of the comparison of the performance of content-based image retrieval systems based on structure [1] with those based on histogram and texture analysis methods, where retrieval is concerned with locating images containing manmade objects. | | | We have shown that the extraction of semantic features describing the structural content of an image provides an advantage over histogram and texture analysis in methodologies where retrieval is based upon the presence of manmade objects in an image. | | | With recent advances in computing technology content-based image retrieval systems are becoming increasingly useful and desirable. |
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http://amazon.ece.utexas.edu/~qasim/papers/SIP99_4/SIP99_4.html
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| | Ariadne 19: Metadata: Image retrieval |
| | Image-based information is a key component of human progress in a number of distinct subject domains and digital image retrieval is a fast-growing research area with regard to both still and moving images. | | | The real challenge of image retrieval would appear to be to develop implementations that were truly user-led rather than technology-led. | | | WebSeer was a system that retrieved images from the Web using information from two sources: the text that relates to the image and the image itself. |
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http://www.ariadne.ac.uk/issue19/metadata
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| | Hidden Annotation in Content Based Image Retrieval |
| | Image matching by means of intensity and texture matching in the Fourier domain. | | | Semantically annotated images are appearing in structured environments such as medical image databases, news organization archives - and the trend seems to extend to generic electronic collections. | | | If a human operator is required to formulate a query using this language, and interpret a database image's description in terms of the language, two serious problems arise. |
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http://www.pnylab.com/pny/papers/hannote/hannote/hannote.html
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| | COMPASS: content-based image retrieval related LINKS |
| | Images are segmented into homogeneous regions at the time of ingest into the database, and image attributes that represent each of these regions are computed. | | | This representation allows the user to compose interesting queries such as "retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper one-third of the image" where the individual objects could be regions belonging to different images. | | | The Institute for Image Data Research at the University of Northumbria at Newcastle is a multi-disciplinary research Institute bringing together the knowledge and skills of researchers from a variety of disciplines, including Computing, Information and Library Management, Psychology and Art History. |
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http://compass.itc.it/links.html
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| | CIRES: Content based Image REtrieval System |
| | Images have been divided into various classes and subclasses for users' convenience and research. | | | Ability to query using any image on the web. | | | Otherwise, CIRES searches the whole image database to retrieve the best matches for any particular image query. |
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http://amazon.ece.utexas.edu/~qasim/research.htm
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| | Object and Concept Recognition for Content-Based Image Retrieval |
| | Content-based image retrieval is not yet a commercial success, because most real users searching for images want to specify the semantic class of the scene or the object(s) it should contain. | | | The goal of this research is to develop the necessary methodology for automated recognition of generic object and concept classes in digital images. | | | Content-based retrieval requires the recognition of generic classes of objects and concepts. |
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http://www.cs.washington.edu/research/imagedatabase
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| | Project Report: Efficient Content-Based Image Retrieval |
| | The area of content-based image retrieval is a hybrid research area that requires knowledge of both computer vision and of database systems. | | | Researchers in computer vision and computer graphics have developed image distance measures that can compare a sample image or sketch provided by a user to the images in the database and retrieve those that are judged similar by the measure being used. | | | We have developed algorithms and data structures for efficient image retrieval from large databases with multiple distance measures. |
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http://www.cs.washington.edu/research/imagedatabase/reportfin.html
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| | SIMPLIcity / ALIP: Object Concept Recognition / Content Based Image Retrieval / Annotation / Search (1995-, WIPE, ... |
| | This content-based image search and automatic learning-based linguistic indexing project was started in 1995 when James developed an art image retrieval system for the Stanford University Libraries. | | | Inspired by the fact that the Riemann Hypothesis remains as one of the most important unsolved problems in mathematics, the RIEMANN group attempts to address the problem of intelligent media annotation, one of the most important unsolved problems in computer and information sciences. | | | He has later worked for the IBM QBIC project, the NEC AMORA project, and the AI Center of SRI International. |
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http://wang.ist.psu.edu/IMAGE
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| | Emedia Professional: Here's Waldo: content-based image retrieval - applications |
| | But image content-based retrieval is well on its way, having moved from the lab to the enterprise and the Web to serve effectively a healthy range of practical graphic media management applications. | | | IBM's image management system, Query by Image Content (QBIC), provides searching of still graphics and video collections based on properties such as shape, texture, sketches, and other attributes. | | | The download package includes the image indexing and search engine (for AIX, Linux, Windows 95/NT, and OS/2), a Web front end, APIs for embedding QBIC in other applications or extending QBIC with new query functions, and even a sample image collection. |
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http://www.findarticles.com/p/articles/mi_m0FXG/is_n2_v11/ai_20179372
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| | Content Based Image Retrieval and Pathology Image Classification |
| | Content Based Image Retrieval and Pathology Image Classification | | | In a collaborative effort with pathologist Michael J. Becich and his team at the University of Pittsburgh Medical Center, PSC is helping to develop computerized methods for classifying and retrieving pathology microscope images. | | | This graphic illustrates use of a query image to retrieve similar images from an online image database (select image above to see a larger display). |
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http://www.psc.edu/research/abstracts/becich.html
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| | Computer Vision Meets Digital Libraries |
| | Use large image collections as data for classical computer vision problems such as object recognition. | | | Specically, we can ask which images have high probability given the query items, which can be any combination of words and image features. | | | Blobworld: Image segmentation using Expectation-Maximization and its application to image querying, in review. |
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http://elib.cs.berkeley.edu/vision.html
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| | Eidetic: Intelligent content-based image retrieval |
| | The main goal of the project is to combine insights in pattern recognition and cognitive ergonomics to yield important progress in content-based image retrieval systems. | | | Furthermore, user-studies will be performed to assess the usability and effectiveness of the system to be (re-)developed. | | | develop a system for describing the layout of objects in an image and use this as a combined query |
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http://www.nici.kun.nl/Projects/p130/index.html
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| | All About Content Based Image Retrieval |
| | This is not necessary related to actual image recognition (analyzing a picture to find out it contains, say, an elephant), but can be implemented using much more brute force pixel-by-pixel image comparison with some added mirror and scaling fuzzyness. | | | I was hoping to share another CBIR demo caled Image-Seek from LTU Technologies. | | | + Organising personal pictures with content analysis technology |
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http://blog.searchenginewatch.com/blog/050505-111315
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| | Content Based Image Retrieval (Overview) |
| | The VIR Image Engine is an extensible framework for building content based image retrieval systems. | | | Chabot has evolved into Cypress (which, surprisingly, seems not to have inherited content based query capability). | | | QBIC (Query By Image Content) Developer IBM Almaden Research Center, San Jose, CA. |
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http://www-student.informatik.uni-bonn.de/~gerdes/CBIR
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| | Attrasoft ImageFinder |
| | Image Classification: Attrasoft ImageFinder looks at several jpg/gif images and classifies images from local drives. | | | For example, find all images that look like this one. | | | Real-time image recognition capability (image-based, not key-word based) |
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http://www.attrasoft.com/imagefinder42/imageretrieval.html
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| | Attrasoft Content-based Image Retrieval/recognition:Sample Images |
| | Satellite Image Recognition: River Recognition 2------ Scaling Or Rotation Symmetry | | | Satellite Image Recognition: River Recognition 1------ Translation Symmetry | | | Satellite Image Recognition: River Recognition 3------ Scaling AND Rotation Symmetry |
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http://www.attrasoft.com/imagelib
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| | Multimedia Seminar: Content-Based Image Retrieval |
| | C.E. Jacobs, A. Finkelstein, and D.H. Salesin, ``Fast multiresolution image querying,'' ACM SIGGRAPH, 1995 | | | - Wavelet transformed and quantized query image (bilevel) | | | J.R. Smith and S.-F. Chang, ``VisualSEEk: A fully automated content-based image query system,'' ACM Multimedia, 1996 |
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http://meru.cecs.missouri.edu/mm_seminar/cont_ret.html
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| | Content-Based Image Retrieval |
| | This content-based image search engine was developed at Stanford University between 1999 and 2000. | | | The images are shown here for research and viewing purposes, please DO NOT download or copy the images without permission from us. | | | The line of research is on-going at Penn State. |
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http://wang14.ist.psu.edu/cgi-bin/zwang/regionsearch_show.cgi
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http://www.article-engine.com
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