Constructing a Novel Pos-neg Manifold for Global-Based Image Classification

  • Rong Zhu
  • Jianhua Yang
  • Yonggang Li
  • Jie Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)


For the task of global-based image classification, we construct an image manifold, i.e., a pos-neg manifold, based on the solving strategies of two-class classification problem, which includes a positive sub-manifold and a negative one. We also present an improved globular neighborhood based locally linear embedding (an improved GNLLE) algorithm, fully taking account of the big differences between the positive and negative category images, thus the data distance calculation defined in the high-dimensional space can be translated into the one on the image manifold with lower dimensionality. Moreover, to simplify the distance measure between two nonlinear sub-manifolds, we put forward a clustering-based method to determine a manifold center for each sub-manifold. Experimental results on the real-world Web images show that the proposed method can improve the classification performance significantly.


Global-based image classification image manifold two-class classification problem distance measure manifold center 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rong Zhu
    • 1
  • Jianhua Yang
    • 2
  • Yonggang Li
    • 1
  • Jie Xu
    • 1
  1. 1.School of Information EngineeringJiaxing UniversityJiaxingChina
  2. 2.School of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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