Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2020-08-05 , DOI: 10.1016/j.patrec.2020.08.005 Mengyin Wang , Xiaoyu Du , Xiangbo Shu , Xun Wang , Jinhui Tang
Social relationships link everyone in human society. Exploring social relationships in still images promotes researches of behaviors or characteristics among persons. Previous literature has discovered that face and body attributes can provide effective semantic information for social relationship recognition. However, they ignore that attributes contribute much differently to the recognition accuracy, and these multi-source attributes may contain redundancies and noises. This work aims to promote social relationship recognition accuracy by abstracting multi-source attribute features more efficiently. To this end, we propose a novel Deep Supervised Feature Selection (DSFS) framework to recognize social relationships in photos, which fuses the deep learning algorithm with l2,1-norm to learn a discriminative feature subset from multi-source features by leveraging the face and body attributes. Experimental results on PIPA-relation dataset qualitatively demonstrate the effectiveness of the proposed DSFS framework.
中文翻译:
用于社会关系识别的深度监督特征选择
社会关系将人类社会中的每个人联系在一起。探索静止图像中的社会关系可以促进人们之间行为或特征的研究。先前的文献已经发现,面部和身体属性可以为社交关系识别提供有效的语义信息。但是,他们忽略了属性对识别精度的贡献大不相同,并且这些多源属性可能包含冗余和噪声。这项工作旨在通过更有效地抽象多源属性特征来提高社会关系识别的准确性。为此,我们提出了一种新颖的深度监督特征选择(DSFS)框架来识别照片中的社交关系,该框架将深度学习算法与l 2,1-norm通过利用面部和身体属性从多源特征中学习区分特征子集。PIPA关系数据集上的实验结果定性地证明了所提出的DSFS框架的有效性。