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

Xueping Su; Hangchi Zhou; Viorel Petrut Draghici; Matthias Rätsch

doi:10.1117/1.JEI.27.3.033036

15 June 2018 Face naming in news images via multiple instance learning and hybrid recurrent convolutional neural network

Xueping Su, Hangchi Zhou, Viorel Petrut Draghici, Matthias Rätsch

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033036 (June 2018). https://doi.org/10.1117/1.JEI.27.3.033036

Abstract

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.

Citation Download Citation

Xueping Su, Hangchi Zhou, Viorel Petrut Draghici, and Matthias Rätsch "Face naming in news images via multiple instance learning and hybrid recurrent convolutional neural network," Journal of Electronic Imaging 27(3), 033036 (15 June 2018). https://doi.org/10.1117/1.JEI.27.3.033036

Received: 1 December 2017; Accepted: 30 May 2018; Published: 15 June 2018

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