  Selecting the most suitable local invariant feature detector for a particular
application has rendered the task of evaluating feature detectors a critical
issue in vision research. Although the literature offers a variety of
comparison works focusing on performance evaluation of image feature detectors
under several types of image transformations, the influence of the scene
content on the performance of local feature detectors has received little
attention so far. This paper aims to bridge this gap with a new framework for
determining the type of scenes which maximize and minimize the performance of
detectors in terms of repeatability rate. The results are presented for several
state-of-the-art feature detectors that have been obtained using a large image
database of 20482 images under JPEG compression, uniform light and blur changes
with 539 different scenes captured from real-world scenarios. These results
provide new insights into the behavior of feature detectors.
