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Title: CANDID: Comparison algorithm for navigating digital image databases

Abstract

In this paper, we propose a method for calculating the similarity between two digital images. A global signature describing the texture, shape, or color content is first computed for every image stored in a database, and a normalized distance between probability density functions of feature vectors is used to match signatures. This method can be used to retrieve images from a database that are similar to an example target image. This algorithm is applied to the problem of search and retrieval for database containing pulmonary CT imagery, and experimental results are provided.

Authors:
;
Publication Date:
Research Org.:
Los Alamos National Lab., NM (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
10139339
Report Number(s):
LA-UR-94-721; CONF-940987-1
ON: DE94009307
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: 7. international conference on scientific database management,Charlottesville, VA (United States),28-30 Sep 1994; Other Information: PBD: 21 Feb 1994
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; IMAGE PROCESSING; C CODES; DATA BASE MANAGEMENT; ALGORITHMS; DIGITAL SYSTEMS; 990200; MATHEMATICS AND COMPUTERS

Citation Formats

Kelly, P M, and Cannon, T M. CANDID: Comparison algorithm for navigating digital image databases. United States: N. p., 1994. Web.
Kelly, P M, & Cannon, T M. CANDID: Comparison algorithm for navigating digital image databases. United States.
Kelly, P M, and Cannon, T M. Mon . "CANDID: Comparison algorithm for navigating digital image databases". United States. https://www.osti.gov/servlets/purl/10139339.
@article{osti_10139339,
title = {CANDID: Comparison algorithm for navigating digital image databases},
author = {Kelly, P M and Cannon, T M},
abstractNote = {In this paper, we propose a method for calculating the similarity between two digital images. A global signature describing the texture, shape, or color content is first computed for every image stored in a database, and a normalized distance between probability density functions of feature vectors is used to match signatures. This method can be used to retrieve images from a database that are similar to an example target image. This algorithm is applied to the problem of search and retrieval for database containing pulmonary CT imagery, and experimental results are provided.},
doi = {},
url = {https://www.osti.gov/biblio/10139339}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1994},
month = {2}
}

Conference:
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