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Learning Balanced Trees for Large Scale Image Classification

  • Tien-Dung MaiEmail author
  • Thanh Duc Ngo
  • Duy-Dinh Le
  • Duc Anh Duong
  • Kiem Hoang
  • Shin’ichi Satoh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

The label tree is one of the popular approaches for the problem of large scale multi-class image classification in which the number of class labels is large, for example, several tens of thousands of labels. In learning stage, class labels are organized into a hierarchical tree, in which each node is associated with a subset of class labels and a classifier that determines which branch to follow; and each leaf node is associated with a single class label. In testing stage, the fact that a test example travels from the root of the tree to a leaf node reduces the test time significantly compared to the approach of using multiple binary one-versus-all classifiers. The balance of the learned tree structure is the key essential of the label tree approach. Previous methods for learning the tree structure use clustering techniques such as k-means or spectral clustering to group confused labels into clusters associated with the nodes. However, the output tree might not be balanced. We propose a method for learning effective and balanced tree structure by jointly optimizing the balance constraint and the confusion constraint. The experimental results on the datasets such as Caltech-256, SUN-397, and ImageNet-1K show that the classification accuracy of the proposed approach outperforms that of other state of the art methods.

Keywords

Leaf Node Class Label Child Node Spectral Cluster Balance Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tien-Dung Mai
    • 1
    Email author
  • Thanh Duc Ngo
    • 1
  • Duy-Dinh Le
    • 1
    • 2
  • Duc Anh Duong
    • 1
  • Kiem Hoang
    • 1
  • Shin’ichi Satoh
    • 2
  1. 1.University of Information Technology, VNU-HCMHo Chi Minh CityVietnam
  2. 2.National Institute of InformaticsChiyoda-ku, TokyoJapan

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