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From Re-identification to Identity Inference: Labeling Consistency by Local Similarity Constraints

  • Svebor KaramanEmail author
  • Giuseppe Lisanti
  • Andrew D. Bagdanov
  • Alberto Del Bimbo
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery.

Keywords

Re-identification Identity inference Conditional random fields Video surveillance 

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Svebor Karaman
    • 1
    Email author
  • Giuseppe Lisanti
    • 1
  • Andrew D. Bagdanov
    • 1
  • Alberto Del Bimbo
    • 1
  1. 1.Media Integration and Communication CenterUniversity of FlorenceFlorenceItaly

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