Constraint-Based Clustering of Image Search Results Using Photo Metadata and Low-Level Image Features

  • Masaharu Hirota
  • Shohei Yokoyama
  • Naoki Fukuta
  • Hiroshi Ishikawa
Part of the Studies in Computational Intelligence book series (SCI, volume 317)


In this paper, we propose a clustering method in order to effectively present image search results on the Web. In order to reflect the difference of image semantics among the images, we use the meta-tags added by social tagging. Furthermore, we use low-level image features and photo metadata in order to consider the image looks and photo-taking conditions. We applied constrained agglomerative clustering method with must-link constraints for better clustering results by using multiple similarity metric. We conducted experiments to demonstrate that the proposed method effectively clusters image search results comparing to the traditional weighted similarity aggregation approach, and in some cases the clustering performance of our approach is better than other existing approaches for clustering tasks on an online photo sharing site.


Cluster Result Image Similarity Image Search Agglomerative Cluster Image Search Result 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Masaharu Hirota
    • 1
  • Shohei Yokoyama
    • 2
  • Naoki Fukuta
    • 2
  • Hiroshi Ishikawa
    • 2
  1. 1.Graduate School of InformaticsShizuoka University 
  2. 2.Department of Computer Science, Faculty of InformaticsShizuoka University 

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