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Topic: Image retrieval

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Cimitirul Bellu

	Image retrieval - Wikipedia, the free encyclopedia
		Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words.
		Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation.
		Another method of image retrieval is content-based image retrieval (CBIR), which aims at avoiding the use of textual descriptions and instead retrieves images based on their visual similarity to a user-supplied query image or user-specified image features.
	en.wikipedia.org /wiki/Image_retrieval (250 words)

	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.
		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.
		Textual information about images can be easily searched using existing technology, but requires humans to personally describe every image in the database.This is impractical for very large databases, or for images that are generated automatically, e.g.
	en.wikipedia.org /wiki/CBIR (1043 words)

	Structured Knowledge Representation for Image Retrieval
		Image retrieval is the problem of selecting, from a repository of images, those images fulfilling to the maximum extent some criterion specified by an end user.
		The composite shape description intuitively stands for a set of images (all containing the given shapes in their relative positions); it can be used either as a query, or as an index for a relevant class of images, to be given some meaningful name.
		Hence, also an image database is a domain of interpretation, and a complex shape C is a subset of such a domain - the images to be retrieved from the database when C is viewed as a query.
	www.cs.cmu.edu /afs/cs/project/jair/pub/volume16/disciascio02a-html/disciascio02a.html (5655 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].
		Images are assigned to one of the three classes using the nearest neighbor classifier.
	amazon.ece.utexas.edu /~qasim/papers/SIP99_4/SIP99_4.html (2315 words)

	Automated Image Retrieval Using Color and Texture
		Recall signifies the proportion of relevant images in the database that are retrieved in response to a query.
		Precision is the proportion of the retrieved images that are relevant to the query [25].
		Extraction of a multicolored region (a) San Francisco color image, (b) colorized image with 73 colors, (c) pixels that are red, white or blue, (d) the extracted red, white and blue color region corresponing to the flag is added to the index.
	www.ctr.columbia.edu /~jrsmith/html/pubs/PAMI/pami_final_1.html (17137 words)

	Image Retrieval with Multiple Regions-of-Interest
		The goal of this project has been to develop a new image retrieval system based on the principle that it is the user who is most qualified to specify the "content" in an image and not the computer.
		The goal of this project was to develop and test a new technique in image retrieval using local image representations, grouping them into multiple user-specificed "regions-of-interest" while preserving their relative spatial relationships in order to build a more powerful search engine for various applications of image database retrieval.
		Image retrieval in general is based on two key components: a set of image features (like color or texture attributes) and a similarity metric (used to compare images).
	mrl.nyu.edu /~biermann/research/imageretrieval (609 words)

	IRM: Integrated Region Matching for Image Retrieval (Site not responding. Last check: 2007-10-01)
		The targeted image retrieval systems represent an image by a set of regions, roughly corresponding to objects, which are characterized by features reflecting color, texture, shape, and location properties.
		Retrieval results with a photo of a hamburger as the query are shown in Figure 4.
		The query image in Figure 5 is difficult to match because objects in the image are not distinctive from the background.
	www-db.stanford.edu /IMAGE/Simplicity/ACM00.1/li (5394 words)

	Image retrieval
		Images recorded initially in your camera's RAW format should be archived as RAWs to retain all the valuable post-processing advantages RAW files afford.
		Variously known as image viewers, browsers or thumbnailers, these invaluable tools are swift and agile enough for day-to-day image housekeeping.
		EXIF image file format used by most digital cameras allows a limited amount of free text to be stored directly in the image file header.) In theory, the latter approach would allow you to locate image files carrying embedded keywords using only the "text within file" search your operating system's file finder probably offers.
	dpfwiw.com /retrieval.htm (3374 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.
		Large image databases are being collected, and images from these collections made available to users in advertising, marketing, entertainment, and other areas where images can be used to enhance the product.
		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/report.html (1567 words)

	ALA \| DIRECT: A Decentralized Image Retrieval System for the National STEM Digital Library
		Since images constitute a vital vehicle for learning (from preschool to higher education), the effective maintenance of rapidly expanding image libraries is a critical goal.
		For global feature-based image retrieval, global color histograms and texture measurements are used to conduct an image query within the digital libraries.
		Local feature image retrieval using color and texture features is similar to image retrieval based on global color histograms and global texture measures, except that features are extracted from segments, not globally from the images.
	www.ala.org /ala/lita/litapublications/ital/2301tang.htm (3873 words)

	Content-based image retrieval method patent invention
		At each step of the retrieval, the user is asked to select a set of images which will participate in the query; and to assign a degree of relevance to each of them.
		S i (3) where Q is the ideal query, n.sub.1 and n.sub.2 are the numbers of positive and negative images in the query respectively, and R.sub.i and S.sub.i are the features of the positive and negative images respectively.
		When performing retrieval, image classes that assign a high membership probability to positive example images are supported, and image classes that assign a high membership probability to negative example images are penalized.
	www.freshpatents.com /Content-based-image-retrieval-method-dt20060525ptan20060112092.php (2833 words)

	Object-Based Image Retrieval (Site not responding. Last check: 2007-10-01)
		The goal of this project is to create image retrieval systems based on the objects that appear in the images.
		Images are returned to the user ranked by the posterior probability of the object class.
		For the windsurfing category, we also used images from sailboarding so that there was a sufficient number of images for testing.
	www.cs.cmu.edu /~dhoiem/projects/obir (269 words)

	Penn State News
		Image retrieval techniques currently in commercial use mostly rely on keywords or descriptions.
		Wang and colleagues have built an experimental image retrieval system, called SIMPLIcity, to validate and demonstrate their methods and have tested it on a database of about 200,000 general-purpose images and an archive of more than 70,000 pathology images.
		The same program may segment another image of a dog into six regions: the dog’s body, the dog’s front legs, the dog’s rear legs, the dog’s eyes, the background and the sky.
	www.psu.edu /ur/2001/imageretrieval.html (820 words)

	COMPASS: content-based image retrieval related LINKS (Site not responding. Last check: 2007-10-01)
		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.
		This project is concerned with algorithms, data structures and image representations for content-based retrieval of images and video sequences from large databases.
	compass.itc.it /links.html (2127 words)

	Image Retrieval using Color and Shape - Jain, Vailaya (ResearchIndex)
		Abstract: This report deals with efficient retrieval of images from large databases based on the color and shape content in images.
		With the increasing popularity of the use of large volume image databases in various applications, it becomes imperative to build an automatic and efficient retrieval system to browse through the entire database.
		CVPIC image retrieval based on block colour co-occurance..
	citeseer.ist.psu.edu /jain96image.html (561 words)

	The CLEF Cross Language Image Retrieval Track (ImageCLEF)
		ImageCLEF is the cross-language image retrieval track which is run as part of the Cross Language Evaluation Forum (CLEF) campaign.
		The ImageCLEF retrieval benchmark was established in 2003 with the aim of evaluating image retrieval from multilingual
		Images can then be retrieved using primitive features based on pixels with form the contents of an image (e.g.
	ir.shef.ac.uk /imageclef (561 words)

	SIMPLIcity / ALIP: Object Concept Recognition / Content Based Image Retrieval / Annotation / Search (1995-, WIPE, ...
		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.
		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.
	wang.ist.psu.edu /IMAGE (453 words)

	ImageCLEFmed - Medical Image Retrieval Challenge Evaluation
		A multilingual image retrieval track started in 2003, and in 2004 a medical image retrieval track was added.
		Image or multimedia retrieval is interesting for the domain of cross-language information retrieval as the media such as images are inherently almost insensitive to language.
		However, the retrieval of images is an often-neglected topic in the information retrieval domain.
	ir.ohsu.edu /image (389 words)

	UCID - Uncompressed Colour Image Database (Site not responding. Last check: 2007-10-01)
		The aim of the Uncompressed Colour Image Database (UCID - pronounced "use-it") is to provide a benchmark dataset for image retrieval where all images were captured and are available in uncompressed form.
		The current version of the database (v2) has over 1300 images together with a ground truth (predefined query images with corresponding model images that should be retrieved).
		It is envisaged that the dataset is used for the evaluation of image retrieval techniques that operate directly in the compressed domain (so-called 4th criterion algorithms) and to investigate the effect image compression has on the performance of CBIR methods.
	vision.doc.ntu.ac.uk /datasets/UCID/ucid.html (217 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.
		The system is able to serve queries ranging from scenes of purely natural objects such as vegetation, trees, sky, etc. to images containing conspicuous structural objects such as buildings, towers, bridges, etc.
		Images have been divided into various classes and subclasses for users' convenience and research.
	amazon.ece.utexas.edu /~qasim/research.htm (160 words)

	Computer Vision Meets Digital Libraries
		The words may be carefully chosen keywords as in the Corel data set, or free-form text in conjunction with natural language pre-processing.
		Conversely, the image features provide information which is often omitted when humans provide the words because it is clearly a visual element.
		Color- and Texture-based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval.
	elib.cs.berkeley.edu /vision.html (678 words)

	Looking for Good Art, Part 2: Image Retrieval (Site not responding. Last check: 2007-10-01)
		Without detailed image content metadata, search options may be limited to the kinds of "advanced" search options you see on Google Images or AltaVista: text in the file name, file size, file format (limited in Google to JPEG, GIF, or PNG), and whether the image is color, grayscale, or fl and white.
		• Content-Based Image Retrieval: An Overview by Theo Gevers and Arnold W. Smeulders (Faculty of Science, University of Amsterdam, June 2003) [http://carol.science.uva.nl/~gevers/pub/overview.pdf].
		Despite the allure of content-based image retrieval, accurate, valid, standardized, and detailed metadata is the key to the precision recall of online art images.
	www.infotoday.com /searcher/oct04/mattison.shtml (2361 words)

	SIMPLIcity: Content Based Image Retrieval / Search (1995-, WIPE, Virtual Microscope, classification, categorization) (Site not responding. Last check: 2007-10-01)
		This content-based image search and automatic learning-based linguistic indexing project was started in 1995 when James Z.
		Wang developed an art image retrieval system for the Stanford University Libraries.
		If you have any questions or comments about the image retrieval project, please send a message to James Z. Wang (jwang AT ist.psu.
	www-db.stanford.edu /IMAGE (349 words)

	The Bayesian Image Retrieval System, PicHunter (ResearchIndex)
		In addition, this document presents the rationale, design, and results of psychophysical experiments that were conducted to address some key issues that arose during PicHunter's development.
		18 Visual image retrieval by elastic deformation of object sket..
		14 Image matching by means of intensity and texture matching in..
	citeseer.ist.psu.edu /cox00bayesian.html (729 words)

	Archive Retrieval (Site not responding. Last check: 2007-10-01)
		This sub-system is used to supply a search to retrieve and display archived images using user defined index fields.
		There is also a web version of this application allowing you search for and fetch images using a simple internet browser.
		A range can be used to display multiple images.
	www.oystersoft.com /sShow/archive.html (74 words)

	Suchbilder - projektbezogene Links
		John P Eakins and Margaret E Graham: Content-based Image Retrieval A report to the JISC Technology, Applications Programme, Institute for Image Data Research, University Newcastle, 1999.
		Ma and B. Manjunath, NETRA: A Toolbox for navigating large image databases, to be presented at the IEEE Intl.
		Ravela and R. Manmatha: Retrieving Images by Appearance, to appear in the Proc.
	www.suchbilder.de /links/imr.html (564 words)

	IVML > People > Yannis Avrithis
		In December 1995 he was accepted from the School of Electrical and Computer Engineering (ECE) of NTUA for a Ph.D thesis in the area of Digital Image Processing, entitled "Video Sequence Analysis for Annotation, Summarization and Content-Based Retrieval," that was completed in March 2001.
		He is currently a senior researcher at the Image, Video and Multimedia Systems Laboratory (IVML) of NTUA, carrying out research in the area of semantic image and video analysis and coordinating R&D activities in Greek national and European projects.
		His research interests include spatiotemporal image / video segmentation and interpretation, knowledge-assisted multimedia analysis, content-based and semantic indexing and retrieval, video summarization, automatic and semi-automatic multimedia annotation, personalization, and multimedia databases.
	www.image.ntua.gr /~iavr (3535 words)

	QBIC Home Page (Site not responding. Last check: 2007-10-01)
		On-line collections of images are growing larger and more common, and tools are needed to efficiently manage, organize, and navigate through them.
		We have developed the QBIC system which lets you make queries of large image databases based on visual image content -- properties such as color percentages, color layout, and textures occurring in the images.
		Check out QBIC's availability in the DB2 Image Extenders, which are components of IBM's scalable, multimedia, Web-enabled DB2 Universal Database.
	wwwqbic.almaden.ibm.com (227 words)

	Medical Image Retrieval (Site not responding. Last check: 2007-10-01)
		CLEF Cross Language Evaluation Forum (with subtask ImageCLEF for image Retrieval)
		FIRE (Flexible Image Retrieval Engine; follow this link instead)
		There exists a mailing list with which you can reach participants of the tutorial and other people interested in medical image retrieval:
	www-i6.informatik.rwth-aachen.de /~keysers/MedicalImageRetrieval (143 words)

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