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Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics

9 years 3 months ago
Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics
Classification of images in many category datasets has rapidly improved in recent years. However, systems that perform well on particular datasets typically have one or more limitations such as a failure to generalize across visual tasks (e.g., requiring a face detector or extensive retuning of parameters), insufficient translation invariance, inability to cope with partial views and occlusion, or significant performance degradation as the number of classes is increased. Here we attempt to overcome these challenges using a model that combines sequential visual attention using fixations with sparse coding. The model’s biologically-inspired filters are acquired using unsupervised learning applied to natural image patches. Using only a single feature type, our approach achieves 78.5% accuracy on Caltech-101 and 75.2% on the 102 Flowers dataset when trained on 30 instances per class and it achieves 92.7% accuracy on the AR Face database with 1 training instance per perso...
Christopher Kanan, Garrison Cottrell
Added 30 Mar 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Christopher Kanan, Garrison Cottrell
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