A note on the challenge of feature selection for image understanding

It is well known that using the correct features for pattern recognition is far more important than using a sophisticated classifier. A high order classifier, given inadequate features, will produce poor results. Low-level formed are combined to form mid-level features, which have much more discriminating power. Yet, the challenge of feature selection is often neglected in the literature. The literature often assumes that given N low-level features there are 2^N-1 ways to use them, which significantly understates the challenge of finding the best features to use and the best ways to combine them. Basic low-level features (input measurements) must be combined in groups to construct features that are relevant for object recognition [1], yet the computational complexity of grouping measurements for input to a pattern recognition system makes the task very difficult. This paper discusses a method for quantifying the total number of ways to group a given number of low-level features for better understanding the feature selection problem.