| Site |
Description |
| Informedia Digital Video Library |
The Informedia Digital Video Library project is a research initiative at Carnegie Mellon University funded by the NSF, DARPA, NASA and others that studies how multimedia digital libraries can be established and used. The Informedia project has pioneered new approaches for automated video and audio indexing, navigaiton, visualization, search and retrieval and embedded them in a system for use in education, information and entertainment environments. Intelligent, automatic mechanisms are being developed to populate the library. Research in the areas of speech recognition, image understanding, and natural language processing supports the automatic preparation of diverse media for full-content and knowledge based search and retrieval. |
| PICASSO |
This is the home page of the PICASSO project, an ongoing research project for Content Based Image Retrieval, carried out at the Visual Information Processing Laboratory under the supervision of Prof. A. Del Bimbo. |
| GNU Image-Finding Tool |
The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS). It enables you to do Query By Example on images, giving you the opportunity to improve query results by relevance feedback. For processing your queries the program relies entirely on the content of the images, freeing you from the need to annotate all images before querying the collection. |
| Photobook from MIT |
Photobook is a tool for performing queries on image databases based on image content. It works by comparing features associated with images, not the images themselves. These features are in turn the parameter values of particular models fitted to each image. These models are commonly color, texture, and shape, though Photobook will work with features from any model. Features are compared using one out of a library of matching algorithms that Photobook provides. In version 5, these include euclidean, mahalanobis, divergence, vector space angle, histogram, Fourier peak, and wavelet tree distances, as well as any linear combination of these. Version 6 allows user-defined matching algorithms via dynamic code loading. |
| Vision and Modeling Group |
The Vision and Modeling Group of the MIT Media Laboratory was formed in 1987 to study problems in computer vision and scene modeling. Since that time, the group has grown from 2 faculty and 4 graduate students to its current size of 3 faculty, 1 research staff, 2 administrative staff, over 27 graduate students, and many more undergraduate students. Information on current research projects can be found in the Projects section and scattered throughout the descriptions in the People section. (From TH) |
| QBIC(TM) -- IBM's Query By Image Content |
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. Such queries use the visual properties of images, so you can match colors, textures and their positions without describing them in words. Content based queries are often combined with text and keyword predicates to get powerful retrieval methods for image and multimedia databases. |
| QBIC Stamp Sample |
QBIC Stamp Sample |
| JPEG |
Home site of the JPEG and JBIG committees |
| Oracle8i |
Oracle8i interMedia, Spatial, Time Series, |
| INFORMATION SERVICES FOR SCIENTIFIC VISUALIZATION |
INFORMATION SERVICES FOR SCIENTIFIC VISUALIZATION |
| COMPASS: an Image Retrieval System for Distributed Databases |
COMPASS: an Image Retrieval System for Distributed Databases |
| An Evaluation on MARS - -- an Image Indexing and Retrieval System |
Along with the development of storage and network technology, more and more digital image collections are appearing. Research on the indexing and retrieval of digital images is carried out by many institutions. Although this research is still in its early stages, some systems of image indexing and retrieval have appeared. This paper describes an evaluation on MARS (Multimedia Analysis and Retrieval System), an image indexing and retrieval system developed by UIUC. While this evaluation is not complete, it provides some suggestions for the future development of image indexing and retrieval systems |
| Attrasoft ImageFinder |
A software develop for content-based image retrieval |
| Content-based Image Retrieval Project |
An image search engine using semantics-sensitive approach. |
| Efficient Content-Based Image Retrieval |
The goal of this research project is to design and implement a system for content-based image retrieval that can 1) provide a large variety of image-distance |
| Content-based Multimedia Data Management and Efficient Remote Access |
A flexible multimedia data management system that treats the embedded semantic information in a multimedia document which can be searched for like conventional data. It has an algorithm for automatic identification of semantic information in a compressed images and video. The system uses an object-relational database: Illustra. |
| Visual Property-based Search Engine for Image Retrieval |
A content-based image database system and WWW image search engine using free text, color histogram, wavelet transform and wavelet-shape method. |
| Progressive Content-Based Retrieval from Satellite Image Archives |
In this paper, we describe an architecture and initial implementation of a progressive framework for storage and retrieval of image and video data from a collection of heterogeneous archives. |
| A Review of Content-Based Image Retrieval Systems |
This report documents a six month investigation into content-based image retrieval (CBIR) software. The study forms part of a joint venture between Manchester Visualization Centre and the Institute for Image Data Research, which aims to investigate the feasibility of content-based image retrieval for the UK Higher Education Community. |
| Combining Color and Spatial Information for Content-based Image Retrieval |
Much of the information stored in digital libraries will contain either images or video, which is difficult to search or browse. Automatic methods for searching image collections make wide use of color histograms, because they are robust to large changes in viewpoint, and can be computed trivially. However, color histograms fail to incorporate spatial information, and therefore tend to give poor results. We have developed several methods for combining color information with spatial layout, while retaining the advantages of histograms. One technique computes the distribution of a given color as a function of the distance between two pixels. The resulting method, which we call a color correlogram, has proven to be quite effective even with very coarsely quantized color information. Another method computes joint histograms of local properties, thus dividing pixels into classes based on both color and spatial properties. Experiments with a database of over 200,000 images demonstrate that these measures perform significantly better than color histograms, especially when the number of images is large. |
| SIMPLIcity |
Semantics-sensitive Integrated Matching for Picture LIbraries |
| Content-based Image Retrieval Demo Page (Query by example) |
Retrieval image by query by example method |
| Content-Based Image Retrieval for Medical Image Database |
To pose a query to the database, the physician circles one or more pathology bearing regions (PBR) in the query image. |
| Progressive Content-Based Retrieval from Satellite Image Archives |
As the volume of satellite imagery continues to grow exponentially, the efficient management of large collections of such data is becoming a serious challenge. In particular, the successful deployment of automated repositories depends crucially on efficient indexing and retrieval of the desired portion of an image using a simultaneous combination of low-level image features, semantics, and metadata. |
| Image Retrieval Using Vector Quantization in Transform Domain |
In this site, it show how VQ (Vector Quantization) can be used in the content-based image retrieval process. The techniques applied are VQ to DCT (Discrete Cosine Transform) coefficients of Satellite, HTML (Hyper Text Mark-up Language), and other artificial/natural images. Histogram of the codewords is then calculated and used as the retrieval decision maker. Experiments show accurate retrieval results using a small training set. |
| Scalable Content-Based Retrieval from Distributed |
Technological advances over the last several years have made it possible to construct a large digital image and video libraries comprising tens of terabytes of online or nearly online data. As a result of the continued proliferation of image and video data,these libraries will grow significantly larger over the next few years. As a consequence, the demand for the capability to provide databases that can effectively support storage, search, retrieval and transmission of this kind of nontraditional data will grow significantly. This paper describes a project currently in progress at the IBM T.J. Watson Research Center that proposes to explore some of these challenges. |
| Wavelet-based Virtual Microscope Demo Page |
Progressive Zooming of Very Large Images using Wavelets |
| Content-based Multimedia Information Retrieval |
Research about image retrieval system |
| Content-based Multimedia Information Retrieval |
Research about image retrieval system |
| Searching for Digital Pictures |
With the advent of scanners, digital cameras and the world wide web, digitized pictures are becoming more and more abundant. Museums are starting to digitize their collections, photo warehouses are using digital images to index their stock and companies selling clip art maintain entirely digital collections of images, not to mention the military, which produces millions of satellite photos every year. With the profusion of digital image collections, it is becoming essential to have an effective way to store and retrieve images from them. |
| Content-based Image Retrieval System - Blobworld |
Blobworld is a system for content-based image retrieval. By automatically segmenting each |
| Integrated Image Retrieval System |
Integrated Image Retrieval System |
| Content-Based Image Retrieval for Medical |
The content-based image retrieval (CBIR) project |
| MetaSEEk: A Content-Based Meta Search Engine for Images |
The purpose of this project is to integrate the different image query systems on the WEB. These systems have different user interfaces with different options and methods of searching for a set of desired images. Each system has its own database. The Integrated Search by Image Content (ISIC)system allows the user to display images and query from different systems on the WEB where the search would be transparent to the user. ISIC does not contain any local databases and works as an agent for querying images from different systems while using their algorithms for the search. The image query systems integrated by ISIC are Virage, VisualSeek and WebSeek from Columbia University, and QBIC from IBM. |
| CONTENT-BASED IMAGE RETRIEVAL |
This joint project between the Institute and the Manchester Visualization Centre, University of Manchester, is funded by the JISC (Joint Information Systems Committee) Technology Applications Programme. Its principal aims are: 1.To report on the current state of the art in content-based image retrieval, with particular emphasis on the capabilities and limitations of current technology, and the extent to which it is likely to prove of practical use to users in higher education and elsewhere, and to identify areas in which further research is required. 2.To undertake an evaluation of available CBIR software systems. 3.To test the applicability of CBIR methods and software systems in a small range of practical application environments, involving representatives of user groups, and in so doing generate a body of relevant experience and knowledge.4.To raise awareness of this technology in the user and developer communities through web-based demonstrator systems, widely disseminated reports and through hosting seminars and workshops. |
| Multimedia Information Retrieval Reducing information overloadMultimedia Information Retrieval Reducing information overload |
Study multimedia query processing, and in particular its implications on database design |
| VISION TEXTURE |
The VisTex database is a collection of texture images. The database was created |
| COBWEB |
COBWEB (Context-based Image Retrieval on the Web) is a research and development project |
| COBWEB |
COBWEB (Context-based Image Retrieval on the Web) is a research and development project |
| Blockworld |
Blobworld is a system for content-based image retrieval. By automatically segmenting each |
| Content Based Image Search Demo Page |
A Content Based Image Search Demo Page developed by Stanford University |
| The Computer Science Division, University of California, Berkeley |
Chabot: Retrieval from a Relational Database of Images |
| World of Oak |
mainly based on |
| Alta Vista |
mainly based color, texture, shape and sketch. |
| WebSEEk |
A Content-Based Image and Video Search and Catalog Tool for the Web |
| PicSOM |
It is a content-based image retrieval (CBIR) system based on the Self-Organizing Map (SOM). |
| VisualSEEk |
VisualSEEk is a content-based visual query system designed for searching for color photographic images by visual features. |
| Image Engineering Laboratory |
The goal of this laboratory is to develop an content-based image retrieval system by using color, texture, shape and spatial frequency information of an image for multimedia data applications. |
| The University of Michigan School of Information Multi-mode Image Retrieval Group |
It engages in developing content-based image retrieval methods to enable users to search for images based on image features, such as colors, shape, and texture. |
| Content-based image retrieval : color and edges |
A simple content-based system that retrieves color images on the basis of their color distributions and edge characteristics. The system uses two retrieval techniques : histogram intersection to compare color distributions and sketch comparison to compare edge characteristics. |
| Content-based image retrieval system |
This demo system allows images to be searched by color, texture and color composition. |
| Image Indexing and Retrieval for Pathology |
Recent advances in networking, visualization, robotics, and computer technology allow today real-time diagnosis, consultation, and education by using images obtained through remote microscopy. We present a new approach in telepathology, the Image Guided Decision Support (IGDS) system, which integrates components for both remote microscope control and decision support. Using the Micro-Controller component the physician can command a robotic microscope from a distance and obtain high-quality images to be used in diagnosis. The image understanding-based Decision Support component of the system locates, retrieves and displays cases which exhibit morphological profiles consistent with the case in question. The IGDS system has a natural man-machine interface containing engines for speech recognition and voice feedback. |
| Multimedia indexing and content-based image retrieval |
The main goal of IMEDIA is to develop content-based image indexing techniques and interactive search and retrieval methods for browsing large multimedia databases by content. |
| MVC Content-Based Image Retrieval |
Research and evaluate the CBIR software systems for Content-Based Image Retrieval |
| Project Web |
A Contend-Based searching using multi-resoluction properties of the wavelet transfrom |
| Computer Vision Group |
Research algorithms for shape indexation of large image databases |
| Nonlinear Model-Based Analysis and Description of Images for Multimedia Applications (NOBLESSE) |
A new nonlinear model-based descriptions of images and image sequences for Multimedia Application. |
| Purdue Robot Vision Lab |
3D Object Recognition and Robotic Bin Picking Research |
| Similarity-based retrieval of objects from 3D image databases |
Research Group of Computer Vision and Artificial Intelligence. Institute of Computer Science and Applied Mathematics - University of Berne ... |
| Integration, Management and Processing of Images for High-end Applications |
CBIR Internet-sites. Contents, Content Based Image Retrieval; main... |
| Integration, Management and Processing of Images for High-end Applications |
CBIR Internet-sites. Contents, Content Based Image Retrieval; main... |
| Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web |
An approach that allows the combination of visual statistics with textual statistics in the vector space representation commonly used in query by image content systems. |
| The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments |
PicHunter project makes four primary contributions to research on content-based image retrieval |
| NETRA: A Content-Based Image Retrieval System |
NETRA uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database |
| An Integrated WWW Image Retrieval System |
Integrate the text-based and content-based techniques into one system |
| Surfimage: a Flexible Content-Based Image Retrieval System |
It uses the query-by-example approach for retrieving images and integrates advanced features such as image signature combination, classification, multiple queries and query refinement |
| NASA Imgae Exchange |
Content-based image retrieval for NASA image |
| AMORE |
Advanced Multimedia Oriented Retrieval Engine |
| ImageRover |
ImageRover is an experimental world wide web image search tool. The technical challenges associated with the ImageRover project are to deal with the staggering scale of the world wide web and to develop effective image representations for very fast search based on image content. |
| Automated Image Retrieval Using Color and Texture |
In this paper we investigate a system for automated content extraction and database searching based upon color and texture features. These features are important because colors and textures are fundamental characteristics of the content of all images, giving this work general application towards databases of images and videos from a variety of domains. |
| Image DB |
Image DB™ |
| Image Database Design Archive |
This Watch Table Definitions document in pdf format contains the basic definitions of the prototype database Tom McIntyre has been working on. A sample of the database output for a selection of watches is also available. You will need to have the Adobe pdf reader to view the definitions file. The sample database is a very large text document. The sample database is also available as a pdf file, but it is difficult to read. |
| Content-Based Image Retrieval for Medical Databases |
Increasing computerization of the process of acquiring and storing diagnostic images requires more sophisticated approaches to image retrieval in order to optimally use the images for improved health-care delivery, physician education and research. 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. In this approach, a physician manually delineates the pathology-bearing regions in an image. The CBIR system then analyzes these regions for their properties, augmented with the spatial relationships of the pathology bearing regions to anatomical landmarks, to index the images into a database and retrieve similar images typically with known diagnoses. This project focuses on the domain of high resolution computed tomography (HRCT) images in patients with a variety of lung diseases. |
| International Symposium on Music Information Retrieval |
Interest in music information retrieval (music IR) is exploding. This is not surprising: music IR has the potential for a wide variety of applications in the educational and academic domains as well as for entertainment. Yet, until now, there has been no established forum specifically for people studying music retrieval. |
| OPHTHALMOLOGY SEARCH ENGINE |
The Ophthalmology Image Database Search Engine is designed to solve the physical and intellectual access problems users of image collections face. The Ophthalmology Image Database is intended for medical residents, clinicians, and researchers in the field of ophthalmolgy. |
| 20/20 Perfect Vision Information Retrieval System |
The 20/20 Information Retrieval System is a powerful search engine for retrieving both data and images. The data can be retrieved using a number of different search routines. |
| Content Based Information Retrieval |
This paper presents an information retrieval scheme to retrieve relevant text items from an information repository |
| Image Retrieval: Past, Present, And Future |
This paper provides a comprehensive survey of the technical achievements in the research area of Image Retrieval, especially Content-Based Image Retrieval, an area so active and prosperous in the past few years. |
| Image Retrieval: Past, Present, And Future |
This paper provides a comprehensive survey of the technical achievements in the research area of Image Retrieval, especially Content-Based Image Retrieval, an area so active and prosperous in the past few years. |
| Image Storage |
Framing the Picture: Standards for Imaging Systems |
| CT is us |
CT is us is created and maintained by The Advanced Medical Imaging Laboratory (AMIL). AMIL is a multidisciplinary team dedicated to research, education, and the advancement of patient care using medical imaging with a focus on spiral CT and 3D imaging. The AMIL is headed by Elliot K. Fishman, M.D., and includes several radiologists, a computer scientist, multimedia developer (Melissa Garland), and a medical illustrator (Frank Corl). The AMIL is part of the Department of Radiology at the Johns Hopkins Medical Institutions in Baltimore, MD. |
| Andrew Barclay's Medical Imaging Pages |
The site provide web databases for non-profit Medical Imaging information The focus of this site is on using Java, JavaScript, Frames, and Tables to build medical image viewers for remote reading of patient scans. |
| Attrasoft Neural Network Application |
Attrasoft builds the largest and the fastest neural networks and "digital nervous system" in the world. The standard software consists of a network of 10,000 digital nervous units, the neurons. Customized neural nets can be enlarged up to 1,000,000 neurons. |
| IMEDIA |
The main goal of IMEDIA is to develop content-based image indexing techniques and interactive search and retrieval methods for browsing large multimedia databases by content. We study "generic" image databases (e.g. the web) as well as "domain-specific" databases (e.g. facial imagery, medical imagery...). These two categories are related respectively to image retrieval and object recognition. In fact, image retrieval is a wider domain that contains object recognition but also integrates user interactions. More generally, the IMEDIA team achieves research, collaborations, and technology transfer on the complex issue of intelligent access to multimedia data streams. |
| COMPASS |
COMPASS is a distributed application for content-based image retrieval using remote databases. The COMPASS system can be used for two main activities: to browse still image databases, and to search image databases similar to a query image. Therefore, the user formulates queries by examples and not a mere caption textual search |
| Content Based Image Retrieval |
To investigate and develop a content based image retrieval system using neural-fuzzy techniques in conjunction with statistical feature extraction techniques. The neural network is trained only once for the queries and not on database of the images. So it is not necessary to retrain the neural network if there is change in the database of the images. The fuzzy logic is used to define the query such as mostly, many and few. |
| Portable Network Graphics (PNG) |
Home site of Image Format with Lossless Compression with basic introductions, applications, and program sources |
| Blobworld |
Blobworld is a system for content-based image retrieval. By automatically segmenting each image into regions which roughly correspond to objects or parts of objects, we allow users to query for photographs based on the objects they contain. |
| IBM's Query By Image Content |
On-line collections of images are growing larger and more common, and tools are needed to efficiently manage, organize, and navigate through them. IBM 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. |
| Photobook |
Photobook is a tool for performing queries on image databases based on image content. It works by comparing features associated with images, not the images themselves. These features are in turn the parameter values of particular models fitted to each image. These models are commonly color, texture, and shape, though Photobook will work with features from any model. Features are compared using one out of a library of matching algorithms that Photobook provides. |
| Information Retrieval Specialist Group (IRSG, BCS) |
Resources and discussions of Information Retrieval Speclist under British Computer Society |
| Annual Conference on Image Retrieval (C.I.R.) |
Annual UK Conference on Image Retrieval with latest topic presentations, research presentations, and publications. |
| BIOMETRICS RESEARCH |
Biometrics refers to the automatic identification of a person based on his/her physiological or behavioral characteristics. PINs and passwords may be forgotten, and token based methods of identification like passports and driver's licenses may be forged, stolen, or lost. Thus biometric systems of identification are enjoying a renewed interest. Various types of biometric systems are being used for real-time identification, the most popular are based on face recognition and fingerprint matching. However, there are other biometric systems that utilize iris and retinal scan, speech, facial thermograms, and hand geometry. |
| DOCUMENT UNDERSTANDING AND CHARACTER RECOGNITION WWW SERVER |
This system serves as a repository for Document Image Understanding and Optical Character Recognition (OCR) information and resources. The server maintains research announcements, bibliographies, mailing lists, source code, technical reports, database information, and internet resources for document understanding, character recogntion and some related domains such as information retrieval. |
| ARTHUR (ART media and text HUb and Retrieval system) |
The Arthur system uses the AMORE image system, developed by NEC USA, Inc. to index and search |
| PICAS, Picture Archives Singapore |
A text and image retrieval system on the web jointly developed by the National Archives of |
| The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments |
This paper presents the theory, design principles, implementation, and performance results of PicHunter, a prototype content-based image retrieval (CBIR) system that has been developed over a period of 3 years |
| DIGITAL IMAGING AND MEDIA TECHNOLOGY INITIATIVE RESEARCH |
The overall goals of the Digital Imaging and Media Technology Initiative program are to utilize digital methods to preserve and to make accessible fragile and under-utilized visual resources, to promote the use of digital media throughout the campus and scholarly community and to conduct research that advances the creation and use of these resources. |
| Image and Sound Retrieval |
Notes on Image and Sound Retrieval |
| PicToSeek: An Image Retrieval System for the WWW |
An image search system by which visual information is automatically collected, indexed and cataloged entirely on the basis of the pictorial content. The system allows for fast on-line image search by combining: (1) visual browsing through the precomputed image catalogue; (2) query by pictorial example; (3) query by image features. |
| The Piction system |
A image retrieval system, developed at the State University of New York at Buffalo, for face identification in captioned images. This system is aimed at identifying human faces from newspaper images, based on the information contained in the image caption. It integrates text-based retrieval with image-based techniques. |
| Webseek at Columnbia University |
Content-based Image and Video Search and Catalog Tool for the Web |
| Tid Bits |
Image Searching on the Web |
| East Shore Technologies |
Finger print Identification - AFIS |
| Mira |
Evaluation Frameworks for Interactive Multimedia Information Retrieval Applications |
| Content-based Image Retrieval |
The aim of this report is to review the current state of the art in content-based image retrieval (CBIR), a technique for retrieving images on the basis of automatically-derived features such as colour, texture and shape. Our findings are based both on a review of the relevant literature and on discussions with researchers and practitioners in the field. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. While the requirements of image users can vary considerably, it can be useful to characterize image queries into three levels of abstraction: primitive features such as colour or shape, logical features such as the identity of objects shown, and abstract attributes such as the significance of the scenes depicted. While CBIR systems currently operate effectively only at the lowest of these levels, most users demand higher levels of retrieval. |
| A Signature for Content-based Image Retrieval Using a Geometrical Transform |
A research on image visual feature using geometrical (Radon) transform for the similarity measurement in content-based image indexing and retrieval. The signature has a property to reflect the geometrical distribution of the image without a sophisticated segmentation. This signature converts the onerous 2-D correlation into 1-D comparison. It calculates the statistical information while preserving geometrical distribution information in the image. The method is robust against noise and efficient in calculation when certain mapping and indexing technologies are used. |
| Content-based Image and Video Retrieval |
Studies on content-based image and video retrieval methods on Indexing Color-Texture Image Patterns, |
| IBM-NASA Satellite Image Explorer |
Image explorer is a content-based image search tool implementing a progressive framework and supporting: Template Matching, Texture Matching, Visualization and Interactive Querying |
| Content-based image retrieval over the internet |
This system is spilt into two parts. The off-line part and the online user part. A tool for the off-line part will collect and transfer images to the central server. Then an image indexing algorithms will process the image in order to extract descriptive features. For the online parts, a user connects to the system through a common web browser and he can then submit queries based on the image information such as size, date, etc. |
| An efficient low-dimensional color indexing scheme for region-based image retrieval |
This is a prototype image retrieval system that allows users to search and retrieve images in the database based on color information. One of the distinctive aspect of the system is that it allows users to localize the information and select image regions of interest as queries, thus providing a more powerful search tool than other retrieval systems that use global image features. |
| Colour Content-Based Image Retrieval |
With databases containing millions of pictures finding the image or set of images you require is a logistical nightmare. How do you find the one painting you want out of a collection of thousands, or the face of the criminal amongst millions? |
| RSIA |
The goal of the project is to intensify research activities in the field of content-based picture retrieval from remote sensing image databases. General and robust image features are used as the indexes in a relational database. Signal-oriented query method will be investigated as well to retrieve and query stored data |
| Image Retrieval / Search Engine (Content Based) |
Image Retrieval / Search Engine (Content Based) WISE and WBIIS Demo |
| IQuest |
This project aims at proposing new efficient and effective methods, based on multi-dimensional indexing, for similarity evaluation of images or sub-regions of imagessub-regions of images. The goal is to index the multimedia data using features (metadata) derived directly from the data. |
| Shape Queries using image databases |
An image database retrieval system which allow users to submit shapes as query objects. |
| Comparison Algorithm for Navigating Digital Image Databases |
A research project facilitates content-based retrieval of digital image from large databases using a query-by-example methology. A user can provide an example to the system and images in the database that are similar to that example are retrieved. |
| JPEG's Compression Technique |
It introduces the encoding, JPEG process, JPEG's compression technique and how JPEG works |
| Excalibur Visual RetrievalWare SDK |
Color / Shape / Texture Content Image Retrieval Demo |
| Content-based Image Retrieval in LCPD: the Leiden 19th-Century Portrait Database |
Visual Search Tools |
| Content-based Image Database Retrieval By Generalized Complex Moments |
Generalized complex moments of a binary image are features invariant to affine transformations. We apply these features to determine the fold number and mirror symmetric properties of binary logos. Experiments show that these features are sufficient to index binary logos since nearly all logos are retrieved correctly by their affine-transformed versions. Currently, we are working indexing and retrieval of color images. |
| Surrey Image/Video Database Retrieval System |
The problem of image/video database retrieval is now well known but a general solution to this problem has still not been found. This situation is not likely to be resolved in the foreseeable future. Many of the attempts reported in the literature use the low-level content of an image, such as colour, texture and shape, to construct an index for that image. The major advantage of such an approach is that little human intervention is required. However, most of these systems only allow a user to query using a complete image with multiple regions and are unable to retrieve similar looking images based on a single region. The system being developed at the University of Surrey takes this more flexible single-region based approach. |
| Hidden Annotation in Content Based Image Retrieval |
Abstract: The Bayesian relevance-feedback approach introduced with the PicHunter system is extended to include hidden semantic attributes. The general approach is motivated and experimental results are presented that demonstrate significant reductions in search times (28-32%) using these annotations. |
| Content-based Image Browsing and Retrieval Using Texture Features |
Query based on texture properties will have many applications in image and multi-media data bases. Here we show our current work on incorporating these features for browsing large satellite images and air photos. This work relates to the UCSB Alexandria digital library project whose goal is to create a digital library of spatially indexed data such as maps, aerial photos and satellite images. Typical images in such a database range from few megabytes to hundreds of megabytes, posing challenging problems in image analysis and visualization of data. Content based retrieval will be very useful in this context in answering queries. |
| Image and Video indexing and Structuring: Related Links |
Related Links to Video Indexing and Structuring Journal/Conference/Workshop site |
| Wavelet-based Image Compression |
Very challenging research topic in multimedia compression technique. |
| Content-based Image Retrieval Technqiue for Large Image Database |
A brief introduction for content-based image retrieval of large image database. |
| Viper Content-based Image Retrieval |
[Technique: a variety of colour and texture features, organised using an inverted file approach] [Benchmark: Precision- and recall- performance evaluation] [Database: Cirus (lcavwww.epfl.ch/~zpecenov/CIRCUS/index.html)] [Code: Multimedia Retrieval Markup Language (MRML) (www.mrml.net) ] |
| Content-based Image Retrieval |
Content-based Image Retrieval |
| Evaluating Content Based Image Retrieval Systems |
Evaluating Content Based Image Retrieval Systems |
| Attrasoft Content-based Image Retrieval/recognition:Sample Images |
Attrasoft Content-based Image Retrieval/recognition:Sample Images |
| Content Based Image Retrieval and Pathology Image Classification |
Content Based Image Retrieval and Pathology Image Classification |
| Progressive Content-Based Retrieval from Satellite Image Archives |
Progressive Content-Based Retrieval from Satellite Image Archives |
| ImageMiner Project |
ImageMiner project, formerly known as IRIS (Image Retrieval for Information System), combines in a new way well-known methods and techniques in computer vision and AI to generate content descriptions of images in a textual form automatically. The textual description is generated by four sub-steps: feature extraction like colors, textures and contours, segmentation, object constitution and interpretation of part-whole relations. |
| ImageScape - WWW Image and Text Search Engines |
An image search engine, developed at Leiden University, use query methods based on images, text and sound. |
| Dartmouth |
escribes a content-based image retrieval algorithm using colour and edges and is positioned to cater foe future electronic publishing by contributing as a powerful image retrieval system. The user can use the system to search an image database for images that convey the desired information or mood; a reader should be able to search a corpus of published work for images that are relevant to his or her needs. Most commercial image retrieval systems associate keywords or text with each image and require the user to enter a keyword or textual description of the desired image. This text-based approach has numerous drawbacks -- associating keywords or text with each image is a tedious task; some image features may not be mentioned in the textual description; some features are nearly impossible to describe with text'; and some features can be described in widely different ways. In an effort to overcome these problems and improve retrieval performance, researchers have focused more and more on content-based image retrieval in which retrieval is accomplished by comparing image features directly rather than textual descriptions of the image features. Features that are commonly used in content-based retrieval include colour, shape, texture and edges. This system uses a simple content-based system that retrieves colour images on the basis of their colour distributions and edge characteristics. The system is implemented in four modules – edge extraction, colour extraction, query processing and user interface. The colour and edge extraction modules construct a set of historgrams and an edge map for each image. No user intervention is required during the extraction process. The query processing module uses histogram intersection to compare histograms and sketch comparison to compare edge maps. The user interface provides a graphical front end. The performance of the system shows some promises especially with respect to colour but falls short of the performance that is required for practical electronic publishing. |
