TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Su, Xueping
A1  - Zhou, Hangchi
A1  - Draghici, Viorel Petrut
A1  - Rätsch, Matthias
T1  - Face naming in news images via multiple instance learning and hybrid recurrent convolutional neural network
JF  - Journal of electronic imaging
N2  - Annotations of subject IDs in images are very important as ground truth for face recognition applications and news retrieval systems. Face naming is becoming a significant research topic in news image indexing applications. By exploiting the uniqueness of name, face naming is transformed to the problem of multiple instance learning (MIL) with exclusive constraint, namely the eMIL problem. First, the positive bags and the negative bags are automatically annotated by a hybrid recurrent convolutional neural network and a distributed affinity propagation cluster. Next, positive instance selection and updating are used to reduce the influence of false-positive bag and to improve the performance. Finally, max exclusive density and iterative Max-ED algorithms are proposed to solve the eMIL problem. The experimental results show that the proposed algorithms achieve a significant improvement over other algorithms.
KW  - hybrid recurrent convolutional neural network
KW  - mulitple instance learning
KW  - face naming
KW  - max exclusive density
KW  - iterative max-exclusive density
KW  - distributed affinity propagation cluster
Y1  - 2018
SN  - 1017-9909
SS  - 1017-9909
U6  - https://doi.org/10.1117/1.JEI.27.3.033036
DO  - https://doi.org/10.1117/1.JEI.27.3.033036
VL  - 27
IS  - 3
SP  - 1
EP  - 26
S1  - 26
PB  - SPIE ; IS & T
CY  - Bellingham, Wash.
ER  -