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Query Types and Visual Concept-Based Post-retrieval Clustering

  • Masashi Inoue
  • Piyush Grover
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

In the photo retrieval task of ImageCLEF 2008, we examined the influence of image representations, clustering methods, and query types in enhancing result diversity. Two types of visual concept vectors and hierarchical and partitioning clustering as post-retrieval clustering methods were compared. We used the title fields in the search topics, and either only the title field or both the title and description fields of the annotations were in English. The experimental results showed that one type of visual concept representation dominated the other except under one condition. Also, it was found that hierarchical clustering can enhance instance recall while preserving the precision when the threshold parameters are appropriately set. In contrast, partitioning clustering degraded the results. We also categorized the queries into geographical and non-geographical, and found that the geographical queries are relatively easy in terms of the precision of retrieval results and post-retrieval clustering also works better for them.

Keywords

Retrieval Model Retrieval Result Image Annotation Cluster Criterion Query Type 
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 2009

Authors and Affiliations

  • Masashi Inoue
    • 1
  • Piyush Grover
    • 2
  1. 1.National Institute of InformaticsJapan
  2. 2.Indian Institute of TechnologyKharagpurIndia

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