3,724 research outputs found
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Fine-grained classification involves distinguishing between similar
sub-categories based on subtle differences in highly localized regions;
therefore, accurate localization of discriminative regions remains a major
challenge. We describe a patch-based framework to address this problem. We
introduce triplets of patches with geometric constraints to improve the
accuracy of patch localization, and automatically mine discriminative
geometrically-constrained triplets for classification. The resulting approach
only requires object bounding boxes. Its effectiveness is demonstrated using
four publicly available fine-grained datasets, on which it outperforms or
achieves comparable performance to the state-of-the-art in classification
Combining Generative Models and Fisher Kernels for Object Recognition
Learning models for detecting and classifying object categories is a challenging problem in machine vision. While
discriminative approaches to learning and classification
have, in principle, superior performance, generative approaches provide many useful features, one of which is
the ability to naturally establish explicit correspondence
between model components and scene features – this, in
turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative approach using ‘Fisher kernels’ [1] which retains most of the desirable properties of generative methods, while increasing the classification performance through a discriminative setting. Furthermore, we demonstrate how this kernel framework can be used to combine different types of features and models into a single classifier. Our experiments, conducted on a number of popular benchmarks, show strong performance improvements over the corresponding generative approach and are competitive with the best results reported in the literature
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