𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A Flexible Image Database System for Content-Based Retrieval

✍ Scribed by Andrew P Berman; Linda G Shapiro


Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
578 KB
Volume
75
Category
Article
ISSN
1077-3142

No coin nor oath required. For personal study only.

✦ Synopsis


There is a growing need for the ability to query image databases based on similarity of image content rather than strict keyword search. As distance computations can be expensive, there is a need for indexing systems and algorithms that can eliminate candidate images without performing distance calculations. As user needs may change from session to session, there is also a need for runtime creation of distance measures. In this paper, we present FIDS, "flexible image database system." FIDS allows the user to query the database based on complex combinations of dozens of predefined distance measures. Using an indexing scheme and algorithms based on the triangle inequality, FIDS can often return matches to the query image without directly comparing the query image to more than a small percentage of the database. This paper describes the technical contributions of the FIDS approach to content-based image retrieval.


πŸ“œ SIMILAR VOLUMES


Using Human Perceptual Categories for Co
✍ Chi-Ren Shyu; Christina Pavlopoulou; Avinash C. Kak; Carla E. Brodley; Lynn S. B πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 602 KB

It is often difficult to come up with a well-principled approach to the selection of low-level features for characterizing images for content-based retrieval. This is particularly true for medical imagery, where gross characterizations on the basis of color and other global properties do not work. A

ASSERT: A Physician-in-the-Loop Content-
✍ Chi-Ren Shyu; Carla E. Brodley; Avinash C. Kak; Akio Kosaka; Alex M. Aisen; Lynn πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 574 KB

It is now recognized in many domains that content-based image retrieval from a database of images cannot be carried out by using completely automated approaches. One such domain is medical radiology for which the clinically useful information in an image typically consists of gray level variations i

Information theoretic similarity measure
✍ John Zachary; S. S. Iyengar πŸ“‚ Article πŸ“… 2001 πŸ› John Wiley and Sons 🌐 English βš– 333 KB πŸ‘ 3 views

## Abstract Content‐based image retrieval is based on the idea of extracting visual features from image and using them to index images in a database. The comparisons that determine similarity between images depend on the representations of the features and the definition of appropriate distance fun

Probabilistic Feature Relevance Learning
✍ Jing Peng; Bir Bhanu; Shan Qing πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 695 KB

Most of the current image retrieval systems use "one-shot" queries to a database to retrieve similar images. Typically a Knearest neighbor kind of algorithm is used, where weights measuring feature importance along each input dimension remain fixed (or manually tweaked by the user), in the computati