Scalable Visual Instance Mining with Instance Graph

@inproceedings{Li2015ScalableVI,
  title={Scalable Visual Instance Mining with Instance Graph},
  author={Wei Li and Changhu Wang and Lei Zhang and Yong Rui and Bo Zhang},
  booktitle={BMVC},
  year={2015}
}
In this paper we address the problem of visual instance mining, which is to automatically discover frequently appearing visual instances from a large collection of images. We propose a scalable mining method by leveraging the graph structure with images as vertices. Different from most existing work that focused on either instance-level similarities or image-level context properties, our graph captures both information. The instance-level information is integrated during the construction of a… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 16 references

Fast computation of min-Hash signatures for image collections

2012 IEEE Conference on Computer Vision and Pattern Recognition • 2012
View 1 Excerpt

ARISTA - image search to annotation on billions of web photos

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition • 2010
View 1 Excerpt

Large-Scale Discovery of Spatially Related Images

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2010
View 1 Excerpt

Bundling features for large scale partial-duplicate web image search

2009 IEEE Conference on Computer Vision and Pattern Recognition • 2009
View 3 Excerpts

Geometric min-Hashing: Finding a (thick) needle in a haystack

2009 IEEE Conference on Computer Vision and Pattern Recognition • 2009
View 1 Excerpt

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