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Discovering Video Clusters from Visual Features and Noisy Tags

  • Arash Vahdat
  • Guang-Tong Zhou
  • Greg Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

We present an algorithm for automatically clustering tagged videos. Collections of tagged videos are commonplace, however, it is not trivial to discover video clusters therein. Direct methods that operate on visual features ignore the regularly available, valuable source of tag information. Solely clustering videos on these tags is error-prone since the tags are typically noisy. To address these problems, we develop a structured model that considers the interaction between visual features, video tags and video clusters. We model tags from visual features, and correct noisy tags by checking visual appearance consistency. In the end, videos are clustered from the refined tags as well as the visual features. We learn the clustering through a max-margin framework, and demonstrate empirically that this algorithm can produce more accurate clustering results than baseline methods based on tags or visual features, or both. Further, qualitative results verify that the clustering results can discover sub-categories and more specific instances of a given video category.

Keywords

Visual Feature Spectral Cluster Event Category Home Video Video Category 
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_34_MOESM1_ESM.pdf (234 kb)
Electronic Supplementary Material (PDF 234 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Arash Vahdat
    • 1
  • Guang-Tong Zhou
    • 1
  • Greg Mori
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada

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