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. For example, a recently proposed method of multi-scale, multi-granular classification has high computational overhead of function evaluation for various candidate models independently before comparison. In contrast, we propose an upper bound based context-inclusive approach that reduces computational overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. We also prove that an upper bound exists for each candidate model and the proposed algorithm is correct. Experimental results using land-use classification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly.