Huang Chang-Qin, Chen Ji-Kai, Pan Yan, Lai Han-Jiang, Yin Jian, Huang Qiong-Hao
IEEE Trans Cybern. 2019 Oct;49(10):3744-3754. doi: 10.1109/TCYB.2018.2850745. Epub 2018 Jul 12.
This paper considers a problem of landmark point detection in clothes, which is important and valuable for clothing industry. A novel method for landmark localization has been proposed, which is based on a deep end-to-end architecture using prior of key point associations. With the estimated landmark points as input, a deep network has been proposed to predict clothing categories and attributes. A systematic design of the proposed detecting system is implemented by using deep learning techniques and a large-scale clothes dataset containing 145 000 upper-body clothing images with landmark annotations. Experimental results indicate that clothing categories and attributes can be well classified by using the detected landmark points, which are associated with regions of interest in clothes (e.g., the sleeves and the collars) and share robust learning representation property with respect to large variances of human poses, nonfrontal views, or occlusion. A comprehensive performance evaluation over two newly released datasets is carried out in this paper, showing that the proposed system with deep architecture for clothing landmark detection outperforms the state-of-the-art techniques.
本文考虑了服装中地标点检测的问题，这对服装行业来说既重要又有价值。提出了一种新颖的地标定位方法，该方法基于一种使用关键点关联先验的深度端到端架构。以估计出的地标点作为输入，提出了一个深度网络来预测服装类别和属性。通过使用深度学习技术和一个包含145000张带有地标注释的上身服装图像的大规模服装数据集，实现了所提出检测系统的系统设计。实验结果表明，利用检测到的地标点可以很好地对服装类别和属性进行分类，这些地标点与衣服上的感兴趣区域（如袖子和领口）相关联，并且在人体姿势、非正面视图或遮挡的较大变化方面具有强大的学习表示特性。本文对两个新发布的数据集进行了全面的性能评估，结果表明所提出的具有深度架构的服装地标检测系统优于现有技术。
