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Intuitive and easily interpretable performance measures, repeatability and matching performance, for local feature detectors and descriptors were introduced by Mikolajczyk et al. [10, 9]. They, however, measured performance in a wide baseline setting that does not correspond to the visual object categorisation problem which is a popular application of the detectors and descriptors. The limitation has been recognised and ad hoc evaluations proposed. To the authors' best knowledge, our work is the first which extends the original repeatability and matching performance measures to the case of object classes. Using the novel evaluation framework we test state-of-the-art detectors and descriptors with the popular Caltech-101 dataset and report the object category level (intra-class) repeatability and matching performances.