Robust classification of objects, faces, and flowers using natural image statistics ; Article #: ; Date of Conference: 13-18 June 2010 ; Date Added to IEEE Xplore: ...

Robust Classification of Objects, Faces, and Flowers. Using Natural Image Statistics. Christopher Kanan and Garrison Cottrell. Department of Computer Science ...

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 ...

Many image processing sytems try to mimic the processing that is known to occur in the early parts of the biological visual system.

We try to develop complete probabilistic model to represent and learn appearance of facial objects in both shape and geometry with respect to a landmark in the ...

Supplementary Material for the Paper: Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. Christopher Kanan and Garrison ...

After 100 train and test fixations, NIM achieved 92.2% accuracy compared to our 100% accuracy. The 102 Flowers dataset consists of 8189 images from 102 flower ...

Kanan, C. & Cottrell, G. W. (2010). Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. In Proceedings of the IEEE Conference ...

Kanan, Christopher and Garrison Cottrell, Robust Classification of Objects, Faces, and Flowers using Natural Image Statistics, In 2010 IEEE Conference on ...