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Proceedings Paper

Retrieval of similar objects in simulation data using machine learning techniques
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Paper Abstract

Comparing the output of a physics simulation with an experiment is often done by visually comparing the two outputs. In order to determine which simulation is a closer match to the experiment, more quantitative measures are needed. This paper describes our early experiences with this problem by considering the slightly simpler problem of finding objects in a image that are similar to a given query object. Focusing on a dataset from a fluid mixing problem, we report on our experiments using classification techniques from machine learning to retrieve the objects of interest in the simulation data. The early results reported in this paper suggest that machine learning techniques can retrieve more objects that are similar to the query than distance-based similarity methods.

Paper Details

Date Published: 28 May 2004
PDF: 8 pages
Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); doi: 10.1117/12.527122
Show Author Affiliations
Erick Cantu-Paz, Lawrence Livermore National Lab. (United States)
Sen-ching S. Cheung, Lawrence Livermore National Lab. (United States)
Chandrika Kamath, Lawrence Livermore National Lab. (United States)

Published in SPIE Proceedings Vol. 5298:
Image Processing: Algorithms and Systems III
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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