Fast and balanced: Efficient label tree learning for large scale object recognition

Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei Fei Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

145 Scopus citations

Abstract

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 and 9 million images. We demonstrate significant improvements in test accuracy and efficiency with less training time and more balanced trees compared to the previous state of the art by Bengio et al.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 24
Subtitle of host publication25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
PublisherNeural Information Processing Systems
ISBN (Print)9781618395993
StatePublished - 2011
Externally publishedYes
Event25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 - Granada, Spain
Duration: Dec 12 2011Dec 14 2011

Publication series

NameAdvances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Other

Other25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
Country/TerritorySpain
CityGranada
Period12/12/1112/14/11

All Science Journal Classification (ASJC) codes

  • Information Systems

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