Fine-grained bird species recognition via hierarchical subset learning
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McCool, Chris
Sanderson, Conrad
Bewley, Alex
Chen, Zetao
Corke, Peter
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Quebec City, Canada
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Abstract
We propose a novel method to improve fine-grained bird species classification based on hierarchical subset learning. We first form a similarity tree where classes with strong visual correlations are grouped into subsets. An expert local classifier with strong discriminative power to distinguish visually similar classes is then learnt for each subset. On the challenging Caltech200-2011 bird dataset we show that using the hierarchical approach with features derived from a deep convolutional neural network leads to the average accuracy improving from 64.5% to 72.7%, a relative improvement of 12.7%.
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Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP)
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© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Image processing
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Ge, Z; McCool, C; Sanderson, C; Bewley, A; Chen, Z; Corke, P, Fine-grained bird species recognition via hierarchical subset learning, 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 561-565