Face naming in news images via multiple instance learning and hybrid recurrent convolutional neural network

• Published in: Journal of electronic imaging Vol. 27; no. 3; p. 033036
• Main Authors: Su, Xueping, Zhou, Hangchi, Draghici, Viorel Petrut, Rätsch, Matthias
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 01.05.2018
• Subjects: distributed affinity propagation cluster; iterative max-exclusive density; face naming; hybrid recurrent convolutional neural network; multiple instance learning; max exclusive density
• ISSN: 1017-9909; 1560-229X
• DOI: 10.1117/1.JEI.27.3.033036

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.