Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition

@inproceedings{Deng2011FastAB,
  title={Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition},
  author={Jia Deng and Sanjeev Satheesh and Alexander C. Berg and Li Fei-Fei},
  booktitle={NIPS},
  year={2011}
}
We present a novel approach to efficiently learn a label tree for large scale classification with many classes. The key contribution of the approach is a technique to simultaneously determine the structure of the tree and learn the classifiers for each node in the tree. This approach also allows fine grained control over the efficiency vs accuracy trade-off in designing a label tree, leading to more balanced trees. Experiments are performed on large scale image classification with 10184 classes… CONTINUE READING
Highly Influential
This paper has highly influenced 15 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 158 citations. REVIEW CITATIONS

4 Figures & Tables

Topics

Statistics

010203020112012201320142015201620172018
Citations per Year

158 Citations

Semantic Scholar estimates that this publication has 158 citations based on the available data.

See our FAQ for additional information.