TY - GEN
T1 - Context-inclusive approach to speed-up function evaluation for statistical queries
T2 - An extended abstract
AU - Gandhi, Vijay
AU - Kang, James M.
AU - Shekhar, Shashi
AU - Ju, Junchang
AU - Kolaczyk, Eric D.
AU - Gopal, Sucharita
PY - 2006
Y1 - 2006
N2 - 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
AB - 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
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U2 - 10.1109/icdmw.2006.52
DO - 10.1109/icdmw.2006.52
M3 - Conference contribution
AN - SCOPUS:70350529082
SN - 0769527027
SN - 9780769527024
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 371
EP - 376
BT - Proceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
ER -