Automatic identification of focus personage in multi-lingual news images
Automatic identification of focus personage in multi-lingual news images
复制标题
多语言新闻图像中焦点人物自动识别
DOI：
10.1007/s11042-020-10254-4
复制
发表时间：
2021-01
影响因子：
3.6
通讯作者：
Matthias Rätsch
中科院分区：
文献类型：
--
作者：
Xueping Su;Danyao Zhu;Jie Ren;Matthias Rätsch
新闻图像中人物ID的标注是新闻检索和推荐系统的基础事实。深度神经网络模型的普适性和准确性优化是提高新闻字符自动识别精度和计算效率的关键技术，这在全球范围内受到越来越多的关注。本文探索了用于多语种新闻中自动焦点人物识别的优化深度神经网络模型。首先，以德国新闻中的焦点人物的人脸图像为正样本，训练焦点人物的人脸模型。接下来，利用递归卷积神经网络（RCNN）+双向长短期记忆（Bi-LSTM）+条件随机场（CRF）的方案来标记焦点名称，并应用RCNN-RCNN编码器-解码器将人名翻译成多种语言。第三，结合局部Gabor二值模式直方图序列（LGBPHS）和RCNN的优点描述人脸特征，并使用迭代量化（ITQ）对编码进行二值化。最后，针对不同的领域建立了名称语义网络。实验在包括大约100，000个新闻图像的数据集上进行。实验结果表明，该方法取得了显着的改善比其他算法。
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.
登录
查看更多内容
DOI：
10.1145/1101149.1101155
发表时间：
2005-11
期刊：
Proceedings of the 13th annual ACM international conference on Multimedia
影响因子：
--
作者：
Jun Yang;Rong Yan;Alexander Hauptmann
通讯作者：
Jun Yang;Rong Yan;Alexander Hauptmann
DOI：
10.1109/ipta.2016.7821013
发表时间：
2016-12
期刊：
2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
影响因子：
--
作者：
Lei Li;Xiaoyi Feng;Z. Boulkenafet;Zhaoqiang Xia;Mingming Li;A. Hadid
通讯作者：
Lei Li;Xiaoyi Feng;Z. Boulkenafet;Zhaoqiang Xia;Mingming Li;A. Hadid
DOI：
10.1109/bigcomp.2019.8679233
发表时间：
2019-04
期刊：
2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
影响因子：
--
作者：
Qianwen Wang;M. Iwaihara
通讯作者：
Qianwen Wang;M. Iwaihara
用于纹理分类的中值鲁棒扩展局部二进制模式
影响因子：
10.6
作者：
Liu, Li;Lao, Songyang;Pietikainen, Matti
通讯作者：
Pietikainen, Matti
DOI：
10.1109/cvpr.2012.6248025
发表时间：
2012-06
期刊：
2012 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Duy-Dinh Le;S. Satoh
通讯作者：
Duy-Dinh Le;S. Satoh