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Selecting the most suitable local invariant feature detector for a particularapplication has rendered the task of evaluating feature detectors a criticalissue in vision research. No state-of-the-art image feature detector workssatisfactorily under all types of image transformations. Although theliterature offers a variety of comparison works focusing on performanceevaluation of image feature detectors under several types of imagetransformation, the influence of the scene content on the performance of localfeature detectors has received little attention so far. This paper aims tobridge this gap with a new framework for determining the type of scenes, whichmaximize and minimize the performance of detectors in terms of repeatabilityrate. Several state-of-the-art feature detectors have been assessed utilizing alarge database of 12936 images generated by applying uniform light and blurchanges to 539 scenes captured from the real world. The results obtainedprovide new insights into the behaviour of feature detectors.

Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database