| Site |
Description |
| Search Engine Watch |
Welcome! Most visitors to Search Engine Watch fall into one of two groups. There are webmasters, web marketers and others involved with creating and promoting web sites. Then there are search engine users, everyone from researchers, librarians and general web surfers who want to know how to find things better using search engines. |
| The Anatomy of a Search Engine |
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. |
| MetaSEEk: A Content-Based Meta Search Engine for Images |
This page allows the user to search for images based on the content from databases located in IBM , Virage and Columbia University servers. The algorithms used are located in the three different web sites. However, the search from the different sites is transparent to the user. |
| Content-based Image Retrieval Project |
This project was initially funded by the National Science Foundation under the TID (Trusted Image Distribution) project. It is part of the NSF Digital Library Initiative II (DLI-II) program. Prof. Gio Wiederhold (Computer Science, Electrical Engineering, and Medicine) is the principal investigator. If you have any questions or comments, please send a message to James Wang. |
| JEREMY S. DE BONET : EXAMPLE DRIVEN IMAGE DATABASE QUERYING |
There exists no way to directly measure the similarity between the content of images. Without the ability to measure similarity, it is impossible to treat images as queryable, searchable, or sortable data. As a result queries for images are typically satisfied by manually searching through all images in the entire image database. To automate this process, new techniques are needed to extract from an image qualities which can be used to make measurements of similarity |
| Instructions for the Demo |
Once you go to the demo, the main area of the screen will display individual trademark patterns, and below each pattern will be a set of either three or four buttons. |
| Attrasoft Image Retrieval |
Image Retrieval: Attrasoft ImageFinder looks at a jpg/gif image(s) and locates similar images from local drives. Image Classification: Attrasoft ImageFinder looks at several jpg/gif images and classifies images |
| Information Retrieval: current projects |
This list includes details of current projects supported by the Library and Information Commission through its Information Retrieval research programme. Details of projects supported by the Commission's other Research Programmes can be found on separate pages. |
| WebSeek |
A Content-Based Image and Video Catalog and |
| WebSeek |
A Content-Based Image and Video Catalog and Search Tool for the Web |
| A Content-Based Image Meta-Search Engine |
This Web Site describes MetaSEEk, a meta-search engine used for retrieving images based on their visual content on the Web. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. |
| Demostration of Content-based image retrieval system |
This demo system allows images to be searched by color, texture and color composition. |
| SaFe |
SaFe is a general system for spatial and feature image search. It provides a framework for searching for and comparing images by the spatial arrangement of regions or objects. In a SaFe query, objects or regions are assigned by the user. These are given properties of spatial location, size and visual features, such as color. The SaFe system finds the images that best match the query. SaFe uses fully automatic tools for region/feature extraction and indexing. SaFe also resolves spatial relationships, which allows the user to position objects relative to each other in a query. Example queries include "find images including a blue region on top and a wide green open region in the bottom (looking for images with blue sky and open grass field)," and "use this spatial pattern of red, white, blue colors to find images containing American Flags." |
| SQUID (Shape Queries Using Image Databases) |
SQUID is an image database retrieval system on the internet which allows users to submit shapes as query objects. There are about 1100 images of marine creatures in the database. Each image shows one distinct species on a uniform background. Every image is processed to recover the boundary contour, which is then represented by three global shape parameters and the maxima of the curvature zero-crossing contours in its Curvature Scale Space image. |
| Automatic Classification and Intelligent Clustering for WWWeb Information Retrieval Systems |
An intelligent interface for a WWWeb legal information retrieval system. |
