Content based image retrieval

Content based image retrieval - Factbites
Where results make sense
About us | Why use us? | Reviews | PR | Contact us

Topic: Content based image retrieval

Note: these results are not from the primary (high quality) database.

In the News (Wed 9 Dec 09)

Northern Trust

Marvin Harrison

Peter Chernin

Gary Locke

Match Play

Penelope Cruz

Mickey Rourke

AR Rahman

Danny Boyle

Hugh Jackman

	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.
		A content-based image retrieval system (CBIRS) is a piece of software that implements CBIR.
		The term CBIR seems to have originated in 1992, when it was used by T. Kato to describe experiments into automatic retrieval of images from a database, based on the colours and shapes present.
	en.wikipedia.org /wiki/CBIR (1019 words)

	Content-Based Image Retrieval for Medical Databases
		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.
		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.
		In CBIR the emphasis is on the development of an efficient and practical database methodology for recognizing and retrieving patterns in medical images that represent pathological processes.
	oldwww.cs.pitt.edu /idm98/Imported/brodley.html (771 words)

	Content-based image retrieval
		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 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.
		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.
	www.ee.surrey.ac.uk /icpr2004/tutorials/Content-basedimageretrieval_000.htm (425 words)

	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.
		Our overall motive is to extend the current stage of content-based image retrieval (CBIR), which is limited to the treatment of lower-level image descriptions, such as histograms of pixel values [3] and texture analysis [4].
		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.
	amazon.ece.utexas.edu /~qasim/papers/SIP99_4/SIP99_4.html (2315 words)

	Hidden Annotation in Content Based Image Retrieval
		Systems that retrieve images based on their content must in some way codify these images so that judgments and inferences may be made in a systematic fashion.
		In the first phase, users were told to select images that they thought were similar to the target, without being told what to base their judgment of similarity upon.
		Individual images were either in ``portrait'' (4.83 x 7.25 cm on the screen) or in ``landscape'' (7.25 x 4.83 cm) format.
	www.pnylab.com /pny/papers/hannote/hannote/hannote.html (2860 words)

	jtap-039.doc
		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.
		CBIR differs from classical information retrieval in that image databases are essentially unstructured, since digitized images consist purely of arrays of pixel intensities, with no inherent meaning.
		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.
	www.jisc.ac.uk /uploaded_documents/jtap-039.doc (20382 words)

	CIRES: Content based Image REtrieval System
		CIRES is a robust content-based image retrieval system based upon a combination of higher-level and lower-level vision principles.
		Images have been divided into various classes and subclasses for users' convenience and research.
		and the corresponding first few most similar images retrieved, please click here.
	amazon.ece.utexas.edu /~qasim/research.htm (160 words)

	Ariadne 19: Metadata: Image retrieval
		CBIR techniques, by contrast, aim to recognise and retrieve information based on the content of images themselves [3].
		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.
		CIR 99 was an useful means of bringing together researchers and practitioners from a wide range of subject domains to discuss the retrieval of digital images.
	www.ariadne.ac.uk /issue19/metadata (1862 words)

	COMPASS: content-based image retrieval related LINKS
		Content based queries are often combined with text and keyword predicates to get powerful retrieval methods for image and multimedia databases.
		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.
		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.
	compass.itc.it /links.html (2127 words)

	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.
		We have developed algorithms and data structures for efficient image retrieval from large databases with multiple distance measures.
		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.
	www.cs.washington.edu /research/imagedatabase/reportfin.html (1220 words)

	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.
		But content-based image retrieval is critical to many applications, and similar in principle to much text retrieval, and useful image search systems are emerging from such vendors as IBM, Excalibur, and Virage.
		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.
	www.findarticles.com /p/articles/mi_m0FXG/is_n2_v11/ai_20179372 (1007 words)

	All About Content Based Image Retrieval
		P.L. has a great summary of a Slashdot post concerning content based image retrieval (CBIR) and research from Penn St. University.
		A CBIR research project of Penn State University has now been applied to an aviation images database, Slashdot reports.
		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.
	blog.searchenginewatch.com /blog/050505-111315 (362 words)

	SIMPLIcity / ALIP: Object Concept Recognition / Content Based Image Retrieval / Annotation / Search (1995-, WIPE, Virtual Microscope, Automatic Linguistic Indexing of Pictures)
		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.
		The copyrights of the images belong to the owners of the images.
		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.
	wang.ist.psu.edu /IMAGE (456 words)

	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.
		Content-based retrieval requires the recognition of generic classes of objects and concepts.
		The large commercial image providers are still using human indexers to select keywords for their images, even though their databases contain thousands or, in some cases, millions of images.
	www.cs.washington.edu /research/imagedatabase (387 words)

	Computer Vision Meets Digital Libraries
		Color- and Texture-based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval.
		Conversely, the image features provide information which is often omitted when humans provide the words because it is clearly a visual element.
		Specically, we can ask which images have high probability given the query items, which can be any combination of words and image features.
	elib.cs.berkeley.edu /vision.html (678 words)

	Content Based Image Retrieval (Overview)
		The VIR Image Engine is an extensible framework for building content based image retrieval systems.
		Eakins, J P and Graham, M E. Content-based image retrieval.
		Chabot has evolved into Cypress (which, surprisingly, seems not to have inherited content based query capability).
	www-student.informatik.uni-bonn.de /~gerdes/CBIR (620 words)

	Attrasoft ImageFinder
		Image Retrieval: Attrasoft ImageFinder looks at a jpg/gif image(s) and locates similar images from local drives.
		Real-time image recognition capability (image-based, not key-word based)
		The central task in any image data management system is to retrieve images that meet some specified constraints.
	www.attrasoft.com /imagefinder42/imageretrieval.html (87 words)

	Word Content : Content is King.
		Having our unique content in your site can help you get up higher in the Search Engine rankings, unlike sites with duplicated, syndicated or free content, who get given red flags by the Search Engines.
		If you need professional copywriters for your content or if you need a team that can deliver the complete package at a reasonable price, look no further.
		Whether the concept for your website is already clearly defined or it is still in the planning stages, get a free quote so that we can go over your project with you and determine the most effective way to realise your objectives.
	www.WordContent.com (210 words)

	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.
	wang14.ist.psu.edu /cgi-bin/zwang/regionsearch_show.cgi (134 words)

	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).
	www.psc.edu /research/abstracts/becich.html (90 words)

	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.
		For this purpose, the current image retrieval system will be used as a basis to combine three different IR techniques: outline-based, feature-based and object-based image retrieval.
		develop a system for describing the layout of objects in an image and use this as a combined query
	www.nici.kun.nl /Projects/p130/index.html (165 words)

	Attrasoft Content-based Image Retrieval/recognition:Sample Images
		Satellite Image Recognition: River Recognition 2------ Scaling Or Rotation Symmetry
		Satellite Image Recognition: River Recognition 3------ Scaling AND Rotation Symmetry
		Satellite Image Recognition: River Recognition 1------ Translation Symmetry
	www.attrasoft.com /imagelib (79 words)

	Article Engine
		Our content management system is a quick and easy to use article management script for adding, updating and editing articles and content on your web site!
		Give them permission to add, edit, and manage any part of the content management system directly from the editors admin area.
		Let's face it - There are hundreds of other Content Management Systems out there.
	www.article-engine.com (183 words)

	Content-Based Image Retrieval for Medical Image Databases at Purdue
		Content-Based Image Retrieval for Medical Image Databases at Purdue
	rvl2.ecn.purdue.edu /~cbirdev/WWW/CBIRmain.html (9 words)

	Multimedia Seminar: Content-Based Image Retrieval
		J.R. Smith and S.-F. Chang, ``VisualSEEk: A fully automated content-based image query system,'' ACM Multimedia, 1996
		Full decomposition, image size 128x128, 7 scales, size at coarsest scale is 1x1 -> overall average intensity
		- Wavelet transformed and quantized query image (bilevel)
	meru.cecs.missouri.edu /mm_seminar/cont_ret.html (268 words)

	Supercomputing, Visualization & e-Science Group (SVE)
		Venters, C. Cooper, M. "Content-Based Image Retrieval: A New Paradigm for Image Retrieval?" 'Spectra', Fall Issue, November 2000.
		Venters, C. Cooper, M. "A Review of Content-Based Image Retrieval Systems" JISC Technology Applications Programme, 2000.
		Venters, C. Hartley, R. Cooper, M. Hewitt, W. "Query by Visual Example: Assessing the Usability of Content-Based Image Retrieval System User Interfaces." In: The Proceedings of the Second IEEE Pacific-Rim Conference on Multimedia: 2001 International Symposium on Multimedia Information Processing.
	www.sve.man.ac.uk /General/Publications (268 words)

	VisInfo -- Project Information --- Image Retrieval Service
		prepared for querying -- using techniques used in ``content-based image retrieval''.
		The implementation is based on own programs as well as on an integration of existing image retrieval techniques and software (IBM's Query-By-Image-Content
		he Image Retrieval Service (IRS) is an additional search facility for the EVlib to search for documents.
	infovis.zib.de /irsinfo.html (268 words)

	» Search Engines and Block Analysis with Image Retrieval - Search Engine News Journal
		Where the engine will capture an image of the page, break the image out into blocks of passages (as would a human) and then assign the appropriate weights to the various blocks of content.
		Search Engines and Block Analysis with Image Retrieval
		The goal is for the engines to look at a page, understand the blocks within the page and then assign appropriate weights to the content and links based on which ‘block’ the content and links are found.
	www.searchenginejournal.com /index.php?p=962 (268 words)

	Who we are - ACT Partners
		During his two years as a School of Information graduate student and a Digital Information Associate Vlad participated in a plethora of Web-based research and development projects such as: Art Image Browser, Instrument Encyclopedia, content-based image retrieval, CHICO, Pewabic Pottery Exhibit, and others.
		Laura Breeden is an independent consultant focusing on Internet strategies and organizational development, based in Menlo Park, California.
		Vlad's varied background and interests include modern languages (he studied German language at the University of Kiel and obtained his BA in German Language and Literature from Eastern Michigan University), programming (in Pascal, Fortran, and C++), education (he holds a Secondary Teaching Certificate in the State of Michigan), electronic publishing, and computer networks.
	www.communitytechnology.org /partners.html (268 words)

	Suchbilder - projektbezogene Links
		NETRA (Web-Demo) : A Content-Based Image Retrieval System / NETRA-2(Webdemo) Alexandria Digital Library Project.
		PicSOM, image browsing system based on the Self-Organizing Map, Helsinki, Online-Demo
		SQUID, Shape Queries Using Image Databases, University of Surrey, demo
	www.suchbilder.de /links/imr.html (268 words)

Try your search on: Qwika (all wikis)

Topic: Content based image retrieval

Ads by Google

Related Topics

In the News (Wed 9 Dec 09)