Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
This work addresses a gap in vision research by evaluating feature detectors based on scene content, though it is incremental as it builds on existing comparison frameworks.
The paper tackles the problem of selecting local invariant feature detectors by analyzing how scene content affects their performance, using a large database of 12,936 images to identify scenes that maximize or minimize repeatability rates.
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. No state-of-the-art image feature detector works satisfactorily under all types of image transformations. Although the literature offers a variety of comparison works focusing on performance evaluation of image feature detectors under several types of image transformation, 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. Several state-of-the-art feature detectors have been assessed utilizing a large database of 12936 images generated by applying uniform light and blur changes to 539 scenes captured from the real world. The results obtained provide new insights into the behaviour of feature detectors.