基于众包的图片标注系统

Image Annotation System Based on Crowdsourcing

在机器视觉系统的训练过程中,需要大量带有标签的图像来增强识别能力。传统的方法是召集一部分人进行独立注释,效率低,质量差。本文设计一个基于众包的图像标注系统。该系统利用协同过滤技术将图片推送给具有相应专业或兴趣爱好的志愿者,然后通过语义处理算法对同一幅图片的标签集进行整理和归类,最后得出准确有效的标签。实验结果表明,该系统比传统图像标注方法有更好的鲁棒性和更高的效率。

In the training process of machine vision system, a large number of labeled images are needed to enhance the recognition ability. The traditional method is to gather a group of people for independent annotation, which is inefficient and of poor quality. This paper designs an image labeling system based on crowdsourcing. The system uses collaborative filtering technology to push pictures to volunteers with corresponding majors or interests, and then sorts and classifies the tag set of the same picture through semantic processing algorithm, and finally obtains accurate and effective tags. Experimental results show that the system has better robustness and higher efficiency than traditional image labeling methods.