Interactive visual object search through mutual information maximization

Jingjing Meng*, Junsong Yuan, Yuning Jiang, Nitya Narasimhan, Venu Vasudevan, Ying Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations


Searching for small objects (e.g., logos) in images is a critical yet challenging problem. It becomes more difficult when target objects differ significantly from the query object due to changes in scale, viewpoint or style, not to mention partial occlusion or cluttered backgrounds. With the goal to retrieve and accurately locate the small object in the images, we formulate the object search as the problem of finding subimages with the largest mutual information toward the query object. Each image is characterized by a collection of local features. Instead of only using the query object for matching, we propose a discriminative matching using both positive and negative queries to obtain the mutual information score. The user can verify the retrieved subimages and improve the search results incrementally. Our experiments on a challenging logo database of 10,000 images highlight the effectiveness of this approach.

Original languageEnglish (US)
Title of host publicationMM'10 - Proceedings of the ACM Multimedia 2010 International Conference
Number of pages4
StatePublished - Dec 1 2010
Event18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 - Firenze, Italy
Duration: Oct 25 2010Oct 29 2010


Other18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10


  • interactive object search
  • localization
  • mutual information maximization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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