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arXiv · 1505.02269

Subset Feature Learning for Fine-Grained Category Classification

Zongyuan Ge·Christopher Mccool·Conrad Sanderson·Peter Corke
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Abstract

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy.

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Zongyuan Ge, Christopher Mccool, Conrad Sanderson, Peter Corke. 2015-05-09. Subset Feature Learning for Fine-Grained Category Classification. https://arxiv.org/abs/1505.02269

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