The algorithm selection has been successfully used on a variety of decision problems. When the problem definition is structured and several algorithms for the same problem are available, then meta-features, that in turn permit a highly accurate algorithm selection on a case-by-case basis, can be easily and at a relatively low cost extracted. Real world problems such as computer vision could benefit from algorithm selection as well, however the input is not structured and datasets are very large both in samples size and sample numbers. Therefore, meta-features are either impossible or too costly to be extracted. Considering such limitations, in this paper we experimentally evaluate the cost and the complexity of algorithm selection on two popular computer vision datasets VOC2012 and MSCOCO and by using a variety task oriented features. We evaluate both dataset on algorithm selection accuracy over five algorithms and by using a various levels of dataset manipulation such as data augmentation, algorithm selector fine tuning and ensemble selection. We determine that the main reason for low accuracy from existing features is due to insufficient evaluation of existing algorithms. Our experiments show that even without meta features, it is thus possible to have meaningful algorithm selection accuracy, and thus obtain processing accuracy increase. The main result shows that using ensemble method, trained on MSCOCO dataset, we can successfully increase the processing result by at least 3% of processing accuracy.