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Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

  • Shrenik Lad
  • Devi Parikh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

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 many such constraints before achieving good clustering performance because each constraint only provides weak cues about the desired clustering. In this paper, we propose to use image attributes as a modality for the user to provide more informative cues. In particular, the clustering algorithm iteratively and actively queries a user with an image pair. Instead of the user simply providing a must-link/cannot-link constraint for the pair, the user also provides an attribute-based reasoning e.g. “these two images are similar because both are natural and have still water” or “these two people are dissimilar because one is way older than the other”. Under the guidance of this explanation, and equipped with attribute predictors, many additional constraints are automatically generated. We demonstrate the effectiveness of our approach by incorporating the proposed attribute-based explanations in three standard semi-supervised clustering algorithms: Constrained K-Means, MPCK-Means, and Spectral Clustering, on three domains: scenes, shoes, and faces, using both binary and relative attributes.

Keywords

Cluster Algorithm Spectral Cluster Soft Constraint Neural Information Processing System Cluster Criterion 
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.

Supplementary material

978-3-319-10599-4_22_MOESM1_ESM.pdf (968 kb)
Electronic Supplementary Material (PDF 969 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shrenik Lad
    • 1
  • Devi Parikh
    • 1
  1. 1.Virginia TechBlacksburgUSA

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