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Learning Discriminative and Shareable Features for Scene Classification

  • Zhen Zuo
  • Gang Wang
  • Bing Shuai
  • Lifan Zhao
  • Qingxiong Yang
  • Xudong Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

In this paper, we propose to learn a discriminative and shareable feature transformation filter bank to transform local image patches (represented as raw pixel values) into features for scene image classification. The learned filters are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative and class-specific information. For each category, a subset of the filters are activated in a data-adaptive manner, meanwhile sharing of filters among different categories is also allowed. Discriminative power of the filter bank is further enhanced by enforcing the features from the same category to be close to each other in the feature space, while features from different categories to be far away from each other. The experimental results on three challenging scene image classification datasets indicate that our features can achieve very promising performance. Furthermore, our features also show great complementary effect to the state-of-the-art ConvNets feature.

Keywords

Feature learning Discriminant analysis Information sharing Scene Classificsion 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhen Zuo
    • 1
  • Gang Wang
    • 1
    • 2
  • Bing Shuai
    • 1
  • Lifan Zhao
    • 1
  • Qingxiong Yang
    • 3
  • Xudong Jiang
    • 1
  1. 1.Nanyang Technological UniversitySingapore
  2. 2.Advanced Digital Sciences CenterSinapore
  3. 3.City University of Hong KongChina

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