TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Su, Xueping A1 - Zhu, Danyao A1 - Ren, Jie A1 - Rätsch, Matthias T1 - Automatic identification of focus personage in multi-lingual news images JF - Multimedia tools and applications : an international journal N2 - Annotations of character IDs in news images are critical as ground truth for news retrieval and recommendation system. Universality and accuracy optimization of deep neural network models constitutes the key technology to improve the precision and computing efficiency of automatic news character identification, which is attracting increased attention globally. This paper explores the optimized deep neural network model for automatic focus personage identification in multi-lingual news. First, the face model of the focus personage is trained by using the corresponding face images from German news as positive samples. Next, the scheme of Recurrent Convolutional Neural Network (RCNN) + Bi-directional Long-Short Term Memory (Bi-LSTM) + Conditional Random Field (CRF) is utilized to label the focus name, and the RCNN-RCNN encoder–decoder is applied to translate names of people into multiple languages. Third, face features are described by combining the advantages of Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and RCNN, and iterative quantization (ITQ) is used to binarize codes. Finally, a name semantic network is built for different domains. Experiments are performed on a dataset which comprises approximately 100,000 news images. The experimental results demonstrate that the proposed method achieves a significant improvement over other algorithms. KW - recurrent convolutional neural network (RCNN) KW - long short-term memory network (LSTM) KW - iterative quantization (ITQ) KW - multi-lingual news Y1 - 2021 SN - 1380-7501 SS - 1380-7501 U6 - https://doi.org/10.1007/s11042-020-10254-4 DO - https://doi.org/10.1007/s11042-020-10254-4 VL - 80 SP - 11015 EP - 11030 S1 - 16 PB - Springer Science + Business Media CY - Dordrecht ER -