We propose a statistical framework for high-level feature (HLF) extraction, which employs scale-invariant feature transform Gaussian mixture models (SIFT GMMs), acoustic features, and maximal figure-of-merit (MFoM). The MeanInfAP of our best run was 0.1679. Our team placed 11th after all of the runs and 4th among all participating teams. Notably, the InfAPs of "Singing" and "People-dancing" were 0.229 and 0.319, respectively, which were the top scores in all of the runs.