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arXiv · 1310.4759

Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012

Christoph Göring·Alexander Freytag·Erik Rodner·Joachim Denzler
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Abstract

In this paper, we tackle the problem of visual categorization of dog breeds, which is a surprisingly challenging task due to simultaneously present low interclass distances and high intra-class variances. Our approach combines several techniques well known in our community but often not utilized for fine-grained recognition: (1) automatic segmentation, (2) efficient part detection, and (3) combination of multiple features. In particular, we demonstrate that a simple head detector embedded in an off-the-shelf recognition pipeline can improve recognition accuracy quite significantly, highlighting the importance of part features for fine-grained recognition tasks. Using our approach, we achieved a 24.59% mean average precision performance on the Stanford dog dataset.

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Christoph Göring, Alexander Freytag, Erik Rodner, Joachim Denzler. 2013-10-17. Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012. https://arxiv.org/abs/1310.4759

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