Sparse Output Coding for Large-Scale Visual Recognition

Bin Zhao, Eric P. Xing; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3350-3357

Abstract


Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhao_2013_CVPR,
author = {Zhao, Bin and Xing, Eric P.},
title = {Sparse Output Coding for Large-Scale Visual Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}