@inproceedings{Freytag14:ESP,
    type = {inproceedings},
    key = {Freytag14:ESP},
    title = {Exemplar-specific Patch Features for Fine-grained Recognition},
    author = {Alexander Freytag and Erik Rodner and Trevor Darrell and Joachim Denzler},
    booktitle = {DAGM German Conference on Pattern Recognition (DAGM-GCPR)},
    year = {2014},
    pages = {144-156},
    abstract = {In this paper, we present a new approach for fine-grained recognition or subordinate categorization, tasks where an algorithm needs to reliably differentiate between visually similar categories, e.g. different bird species. While previous approaches aim at learning a single generic representation and models with increasing complexity, we propose an orthogonal approach that learns patch representations specifically tailored to every single test exemplar. Since we query a constant number of images similar to a given test image, we obtain very compact features and avoid large-scale training with all classes and examples. Our learned mid-level features are build on shape and color detectors estimated from discovered patches reflecting small highly discriminative structures in the queried images. We evaluate our approach for fine-grained recognition on the CUB-2011 birds dataset and show that high recognition rates can be obtained by model combination.},
    code = {https://github.com/cvjena/patchDiscovery},
    groups = {featurelearning,finegrained,lifelonglearning},
}