Locating Regions of Interest in CBIR with Multi-instance Learning Techniques

  • Zhi-Hua Zhou
  • Xiao-Bing Xue
  • Yuan Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-instance learning techniques. In detail, this paper proposes the CkNN-ROI algorithm, which regards each image as a bag comprising many instances and picks from positive bag the instance that has great chance to meet the target concept to help locate ROI. Experiments show that the proposed algorithm can efficiently locate ROI in CBIR process.


Color Pattern Natural Scene Target Concept CBIR System Diverse Density 
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 2005

Authors and Affiliations

  • Zhi-Hua Zhou
    • 1
  • Xiao-Bing Xue
    • 1
  • Yuan Jiang
    • 1
  1. 1.National Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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