基于条纹的服装分割

J. Lorenzo-Navarro, M. C. Santana, David Freire-Obregón, E. Ramón-Balmaseda
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引用次数: 1

摘要

本文描述了一种用于时尚分析的服装分割方法。该方法不依赖于先前的姿态估计,而是依赖于人的分割。因此,为了实现准确的人物分割,人们考虑并改进了新的和经典的分割技术。与文献中描述的其他方法不同,输出的是边界框和不同衣服的主要颜色,而不是像素级分割。该方案基于将人体区域划分为初始固定数量的条纹,然后根据相似的颜色分布将这些条纹融合在一起。为了评估所提出方法的质量,实验使用了在时尚解析社区中广泛使用的Fashionista数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stripe based clothes segmentation
In this paper, a clothes segmentation method for fashion parsing is described. This method does not rely in a previous pose estimation but people segmentation. Therefore, novel and classic segmentation techniques have been considered and improved in order to achieve accurate people segmentation. Unlike other methods described in the literature, the output is the bounding box and the predominant color of the different clothes and not a pixel level segmentation. The proposal is based on dividing the person area into an initial fixed number of stripes, that are later fused according to similar color distribution. To assess the quality of the proposed method the experiments are carried out with the Fashionista dataset that is widely used in the fashion parsing community.
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