Fast Label Propagation on Facial Images Using a Pruned Similarity Matrix

Efstratios Kakaletsis, Olga Zoidi, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
270 Downloads (Pure)


The label propagation process, which is often used to semantically annotate (tag) large amounts of multimedia data assets must be fast, in order to be efficient. In this paper, a novel facial images fast labeling method that is essentially a semisupervised face recognition approach, is presented. The proposed method is based on the acceleration of a state of the art facial identity label propagation technique. The new method is called pruned label propagation due to the fact that the facial label inference is conducted using a similarity matrix containing fewer entries, namely the pairwise similarities that reside in the main and the off-diagonals of this matrix. Experiments conducted on facial image labeling in three stereoscopic movies, confirm the increased labeling accuracy and the reduced computational complexity of the proposed method.
Original languageEnglish
Title of host publicationDigital Media Industry & Academic Forum (DMIAF)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-5090-1000-4
ISBN (Print)978-1-5090-1001-1
Publication statusPublished - 26 Sep 2016
EventIEEE Digital Media Industry and Academic Forum - Santorini, Greece
Duration: 4 Jul 20166 Jul 2016


ConferenceIEEE Digital Media Industry and Academic Forum
Abbreviated titleDMIAF


  • Motion pictures
  • Trajectory
  • Labeling
  • Computational complexity
  • Videos
  • Face recognition


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