Computer Science ›› 2018, Vol. 45 ›› Issue (9): 299-302.doi: 10.11896/j.issn.1002-137X.2018.09.050

Special Issue: Face Recognition

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Research on Face Tagging Based on Active Learning

SUN Jin, CHEN Ruo-yu, LUO Heng-li   

  1. School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2017-07-03 Online:2018-09-20 Published:2018-10-10

Abstract: In the era of big data,tremendous images are available,whereas images with tags are sparse relatively.For the purpose of learning and research,it’s necessary to classify and annotate images,andmost images are relevant to faces,consequently face tagging is an effective tool to annotate images.However,the cost of manual annotation is high.Aiming at solving the problems of lacking tagged images and high manual annotation cost,a discriminative model based on the active learning inducing the posterior distribution over labels was proposed.The discriminative model is based on markov random field(MRF) and gaussian process(GP),and considers the objective connections between the positions and features of samples with the addition of match constraint and non-match constraint between samples.Match constraint means that samples have the same label,while non-match constraint means that samples have different labels.Experimental results indicate that choosing samples for manual annotation according to the posterior distribution over labels induced by the discriminative model can greatly improve the classification accuracy.

Key words: Active learning, Face tagging, Match constraint, Non-match constraint

CLC Number: 

  • TP391.4
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