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Proceedings Paper

Query by image example: the comparison algorithm for navigating digital image databases (CANDID) approach
Author(s): Patrick M. Kelly; T. Michael Cannon; Donald R. Hush
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Paper Abstract

CANDID (comparison algorithm for navigating digital image databases) was developed to enable content-based retrieval of digital imagery from large databases using a query-by- example methodology. A user provides an example image to the system, and images in the database that are similar to that example are retrieved. The development of CANDID was inspired by the N-gram approach to document fingerprinting, where a `global signature' is computed for every document in a database and these signatures are compared to one another to determine the similarity between any two documents. CANDID computes a global signature for every image in a database, where the signature is derived from various image features such as localized texture, shape, or color information. A distance between probability density functions of feature vectors is then used to compare signatures. In this paper, we present CANDID and highlight two results from our current research: subtracting a `background' signature from every signature in a database in an attempt to improve system performance when using inner-product similarity measures, and visualizing the contribution of individual pixels in the matching process. These ideas are applicable to any histogram-based comparison technique.

Paper Details

Date Published: 23 March 1995
PDF: 11 pages
Proc. SPIE 2420, Storage and Retrieval for Image and Video Databases III, (23 March 1995); doi: 10.1117/12.205289
Show Author Affiliations
Patrick M. Kelly, Los Alamos National Lab. (United States)
T. Michael Cannon, Los Alamos National Lab. (United States)
Donald R. Hush, Univ. of New Mexico (United States)


Published in SPIE Proceedings Vol. 2420:
Storage and Retrieval for Image and Video Databases III
Wayne Niblack; Ramesh C. Jain, Editor(s)

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