针对如何利用人脸图像局部特征进行亲属关系认证的问题,文中提出基于局部特征融合的邻域排斥度量学习亲属关系认证算法.首先抽取脸部的关键区域,分别对每块关键区域提取纹理和肤色特征.然后进行特征融合.最后引入度量学习,学习能使具有...针对如何利用人脸图像局部特征进行亲属关系认证的问题,文中提出基于局部特征融合的邻域排斥度量学习亲属关系认证算法.首先抽取脸部的关键区域,分别对每块关键区域提取纹理和肤色特征.然后进行特征融合.最后引入度量学习,学习能使具有亲属关系样本距离变小、非亲属关系样本距离变大的变换矩阵,利用已有数据样本间相似程度的先验知识学习最佳相似性度量,更好地刻画亲属样本间的相似关系.在Kin Face W-I和KinFace W-II数据库中的实验表明,相比已有的亲属关系认证算法,文中算法性能更好.展开更多
Person re-identification has emerged as a hotspot for computer vision research due to the growing demands of social public safety requirements and the quick development of intelligent surveillance networks.Person re-i...Person re-identification has emerged as a hotspot for computer vision research due to the growing demands of social public safety requirements and the quick development of intelligent surveillance networks.Person re-identification(Re-ID)in video surveillance system can track and identify suspicious people,track and statistically analyze persons.The purpose of person re-identification is to recognize the same person in different cameras.Deep learning-based person re-identification research has produced numerous remarkable outcomes as a result of deep learning's growing popularity.The purpose of this paperis to help researchers better understand where person re-identification research is at the moment and where it is headed.Firstly,this paper arranges the widely used datasets and assessment criteria in person re-identification and reviews the pertinent research on deep learning-based person re-identification techniques conducted in the last several years.Then,the commonly used method techniques are also discussed from four aspects:appearance features,metric learning,local features,and adversarial learning.Finally,future research directions in the field of person re-identification are outlooked.展开更多
Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existi...Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shot learning image classification methods only use top-level features and adopt a single measure. In that way, the local features of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure(ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space to compare the similarity between the samples in the local features, and finally integrates the similarity scores from the feature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details and global information of different grains. Experimental results on different fine-grained datasets show that the proposed method achieves good classification performance and also proves its rationality.展开更多
文摘针对如何利用人脸图像局部特征进行亲属关系认证的问题,文中提出基于局部特征融合的邻域排斥度量学习亲属关系认证算法.首先抽取脸部的关键区域,分别对每块关键区域提取纹理和肤色特征.然后进行特征融合.最后引入度量学习,学习能使具有亲属关系样本距离变小、非亲属关系样本距离变大的变换矩阵,利用已有数据样本间相似程度的先验知识学习最佳相似性度量,更好地刻画亲属样本间的相似关系.在Kin Face W-I和KinFace W-II数据库中的实验表明,相比已有的亲属关系认证算法,文中算法性能更好.
文摘Person re-identification has emerged as a hotspot for computer vision research due to the growing demands of social public safety requirements and the quick development of intelligent surveillance networks.Person re-identification(Re-ID)in video surveillance system can track and identify suspicious people,track and statistically analyze persons.The purpose of person re-identification is to recognize the same person in different cameras.Deep learning-based person re-identification research has produced numerous remarkable outcomes as a result of deep learning's growing popularity.The purpose of this paperis to help researchers better understand where person re-identification research is at the moment and where it is headed.Firstly,this paper arranges the widely used datasets and assessment criteria in person re-identification and reviews the pertinent research on deep learning-based person re-identification techniques conducted in the last several years.Then,the commonly used method techniques are also discussed from four aspects:appearance features,metric learning,local features,and adversarial learning.Finally,future research directions in the field of person re-identification are outlooked.
基金supported by the National Natural Science Foundation of China(62176110,62111530146,61906080)Young Doctoral Fund of Education Department of Gansu Province(2021QB-038)。
文摘Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shot learning image classification methods only use top-level features and adopt a single measure. In that way, the local features of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure(ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space to compare the similarity between the samples in the local features, and finally integrates the similarity scores from the feature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details and global information of different grains. Experimental results on different fine-grained datasets show that the proposed method achieves good classification performance and also proves its rationality.
