Probabilistic Label Trees for Efficient Large Scale Image Classification

被引：61

作者：

Liu, Baoyuan ^{[1
]}

Sadeghi, Fereshteh ^{[1
]}

Tappen, Marshall ^{[1
]}

Shamir, Ohad ^{[2
]}

Liu, Ce ^{[2
]}

机构：

[1] Univ Cent Florida, Orlando, FL 32816 USA

[2] Microsoft Res, Boston, NE USA

来源：

2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2013年

关键词：

D O I：

10.1109/CVPR.2013.114

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Large-scale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. The label tree model integrates classification with the traversal of the tree so that complexity grows logarithmically. In this paper we show how the parameters of the label tree can be found using maximum likelihood estimation. This new probabilistic learning technique produces a label tree with significantly improved recognition accuracy.

引用

页码：843 / 850

页数：8

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