Discriminative dictionary learning has been widely used in many applications such as face retrieval / recognition and image classification, where the labels of the training data are utilized to improve the discriminative power of the learned dictionary. This paper deals with a new problem of learning a dictionary for associating pairs of images in applications such as face image retrieval. Compared with a typical supervised learning task, in this case the labeling information is very limited (e.g. only some training pairs are known to be associated). Further, associated pairs may be considered similar only after excluding certain regions (e.g. sunglasses in a face image). We formulate a dictionary learning problem under these considerations and design an algorithm to solve the problem. We also provide a proof for the convergence of the algorithm. Experiments and results suggest that the proposed method is advantageous over common baselines.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781509060672
StatePublished - Aug 28 2017
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: Jul 10 2017Jul 14 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo


Other2017 IEEE International Conference on Multimedia and Expo, ICME 2017
Country/TerritoryHong Kong
CityHong Kong


  • Constrained dictionary learning
  • Face retrieval
  • Non-negative

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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