Fast label propagation on facial images using a pruned similarity matrix
Efstratios Kakaletsis, Olga Zoidi, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas · 2016
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 semi-supervised 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.