Retrieval of similar objects in simulation data using machine learning techniques

Erick Cantú-Paz, Sen Ching S. Cheung, Chandrika Kamath

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)251-258
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2004
EventImaging Processing: Algorithms and Systems III - San Jose, CA, United States
Duration: Jan 19 2004Jan 20 2004


  • Classification
  • Machine learning
  • Similarity-based object retrieval
  • Simulation data
  • Turbulence

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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