Context-inclusive approach to speed-up function evaluation for statistical queries: An extended abstract

Vijay Gandhi, James M. Kang, Shashi Shekhar, Junchang Ju, Eric D. Kolaczyk, Sucharita Gopal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Many statistical queries such as maximum likelihood estimation involve finding the best candidate model given a set of candidate models and a quality estimation function. This problem is common in important applications like land-use classification at multiple spatial resolutions from remote sensing raster data. Such a problem is computationally challenging due to the significant computation cost to evaluate the quality estimation function for each candidate model. A recently proposed method of multiscale, multigranular classification has high computational overhead of function evaluation for various candidate models independently before comparison. In contrast, we propose a context-inclusive approach that controls the computational overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. Experimental results using land-use classification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly while providing comparable classification accuracy

Original languageEnglish (US)
Title of host publicationProceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages371-376
Number of pages6
ISBN (Print)0769527027, 9780769527024
DOIs
StatePublished - 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

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