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arXiv · 1909.07075

Classification-Specific Parts for Improving Fine-Grained Visual Categorization

Dimitri Korsch·Paul Bodesheim·Joachim Denzler
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Abstract

Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts. We propose a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions. The subsequently detected parts are then not only selected by a-posteriori classification knowledge, but also have an intrinsic spatial extent that is determined automatically. This is in contrast to most part-based approaches and even to available ground-truth part annotations, which only provide point coordinates and no additional scale information. We show in our experiments on various widely-used fine-grained datasets the effectiveness of the mentioned part selection method in conjunction with the extracted part features.

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Dimitri Korsch, Paul Bodesheim, Joachim Denzler. 2019-09-16. Classification-Specific Parts for Improving Fine-Grained Visual Categorization. https://doi.org/10.1007/978-3-030-33676-9_5

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