The effect of indexing on the complexity of object recognition

An examination is made of the problem of selecting models from a library, and the combinatorics of determining that a candidate object is not present in the data are examined. It is shown that the expected search is again exponential, implying that naive approaches to indexing are likely to carry an expensive overhead, since exponential work is needed to weed out each incorrect model. The analytical results are shown to be in agreement with empirical data for cluttered object recognition