Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

@inproceedings{Lad2014InteractivelyGS,
  title={Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations},
  author={Shrenik Lad and Mark Johnson},
  booktitle={ECCV},
  year={2014}
}
Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and which ones do not (cannot-link). These approaches require… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 35 REFERENCES

Active image clustering: Seeking constraints from humans to complement algorithms

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Attribute and simile classifiers for face verification

  • 2009 IEEE 12th International Conference on Computer Vision
  • 2009
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

SUN attribute database: Discovering, annotating, and recognizing scene attributes

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition
  • 2012
VIEW 1 EXCERPT