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Combining Randomization and Discrimination for Fine-Grained Image Categorization

8 years 2 months ago
Combining Randomization and Discrimination for Fine-Grained Image Categorization
In this paper, we study the problem of fine-grained image categorization. The goal of our method is to explore fine image statistics and identify the discriminative image patches for recognition. We achieve this goal by combining two ideas, discriminative feature mining and randomization. Discriminative feature mining allows us to model the detailed information that distinguishes different classes of images, while randomization allows us to handle the huge feature space and prevents over-fitting. We propose a random forest with discriminative decision trees algorithm, where every tree node is a discriminative classifier that is trained by combining the information in this node as well as all upstream nodes. Our method is tested on both subordinate categorization and activity recognition datasets. Experimental results show that our method identifies semantically meaningful visual information and outperforms stateof-the-art algorithms on various datasets.
Bangpeng Yao, Aditya Khosla, Li Fei-Fei
Added 08 Apr 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Bangpeng Yao, Aditya Khosla, Li Fei-Fei
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