Feature Evaluation with High-Resolution Images

  • Kai CordesEmail author
  • Lukas Grundmann
  • Jörn Ostermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


The extraction of scale invariant image features is a fundamental task for many computer vision applications. Features are localized in the scale space of the image. A descriptor is build for each feature which is used to determine the correspondence to a second feature, usually extracted from a second image. For the evaluation of detectors and descriptors, benchmark image sets are used. The benchmarks consist of image sequences and homographies which determine the ground truth for the mapping between the images. The repeatability criterion evaluates the detection accuracy of the detectors while precision and recall measure the quality of the descriptors.

Current data sets provide images with resolutions of less than one megapixel. A recent data set provides challenging images and highly accurate homographies. It allows for the evaluation at different image resolutions with the same scene content. Thus, the scale invariant properties of the extracted features can be examined. This paper presents a comprehensive evaluation of state of the art detectors and descriptors on this data set. The results show significant differences compared to the standard benchmark. Furthermore, it is shown that some detectors perform differently on different resolutions. It follows that high resolution images should be considered for future feature evaluations.


Feature Evaluation Scale Invariant Feature Large Resolution Local Image Descriptor British Machine Vision Conference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Leibniz Universität HannoverHanoverGermany

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