Visual Affinity Propagation Improves Sub-topics Diversity without Loss of Precision in Web Photo Retrieval

  • Hervé Glotin
  • Zhong-Qiu Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)


This paper demonstrates that Affinity Propagation (AP) outperforms Kmeans for sub-topic clustering of web image retrieval. A SVM visual images retrieval system is built, and then clustering is performed on the results of each topic. Then we heighten the diversity of the 20 top results, by moving into the top the image with the lowest rank in each cluster. Using 45 dimensions Profile Entropy visual Features, we show for the 39 topics of the imageCLEF08 web image retrieval clustering campaign on 20K IAPR images, that the Cluster-Recall (CR) after AP is 13% better than the baseline without clustering, while the Precision stays almost the same. Moreover, CR and Precision without clustering are altered by Kmeans. We finally discuss that some high-level topics require text information for good CR, and that more discriminant visual features would also allow Precision enhancement after AP.


Web Image Retrieval Diversity Clustering Profile Entropy Features Affinity Propagation XML SVM 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hervé Glotin
    • 1
  • Zhong-Qiu Zhao
    • 1
    • 2
  1. 1.Systems & Information Sciences Lab.UMR CNRS 6168, & Univ. Sud Toulon-VarFrance
  2. 2.Computer & Information SchoolHefei Univ. of TechnologyChina

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