Learning Distance Metrics with Feature Space Performance for Image Retrieval

  • Xin Luo
  • Guowen Wu
  • Kenji Kita
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 288)


Learning from samples in cases where many high-dimensional vectors but only few samples are available is commonly considered a challenging problem in content-based image retrieval (CBIR). In this paper, we propose an algorithm for metric learning based on spatial distribution of image features. The optimal distance metric is then found by minimizing the divergence between the two distributions. The key idea is to construct a global metric matrix that minimizes the cluster distortions, namely, one that reduces high variances and expands low variances for the data to make a spherical form as good as possible in the high-dimensional data spaces. Experimental results show that our approach is effective in improving the performance of CBIR systems.


CBIR Image feature space Cluster geometry Learning distance metric 



This research was partially supported by “the Fundamental Research Funds for the Central Universities (No. 13D11205).”


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyDonghua UniversitySongjiang DistrictChina
  2. 2.Faculty and School of EngineeringThe University of TokushimaTokushimaJapan

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