Context-Inclusive Approach to Speed-up Function Evaluation for Statistical Queries

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.