计算机与现代化

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基于众包的图片标注系统

  

  1. (1.河海大学海岸灾害及防护教育部重点实验室,江苏南京210098; 2.河海大学计算机与信息学院,江苏南京210098;
      3.南通河海大学海洋与近海工程研究院,江苏南通226300)
  • 收稿日期:2019-05-24 出版日期:2019-08-15 发布日期:2019-08-16
  • 作者简介:陈峙宇(1998-),男,江苏盐城人,本科生,研究方向:计算机视觉,深度学习,E-mail: 939187596@qq.com; 吕坦悦(1995-),女,山西陵川人,硕士研究生,研究方向:计算机视觉,机器学习,E-mail: 616156542@qq.com; 王菲(1997-),女,本科生,研究方向:计算机视觉,E-mail: wangfei64218@163.com; 段震伟(1996-),男,学士,研究方向:计算机视觉,E-mail: 1018462681@qq.com。
  • 基金资助:
    河海大学海岸灾害及防护教育部重点实验室开放基金资助项目(201905); 南通市科技计划项目(GY12017014)

Image Annotation System Based on Crowdsourcing

  1. (1. Key Laboratory of Coastal Disaster Prevention and Protection, Ministry of Education, Hohai University, Nanjing 210098, China;
      2. College of Computer and Information, Hohai University, Nanjing 210098, China;
      3. Institute of Ocean and Offshore Engineering, Nantong Hohai University, Nantong 226300, China)
  • Received:2019-05-24 Online:2019-08-15 Published:2019-08-16

摘要: 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。

关键词: 图片标注系统, 协同过滤算法, 标签语义处理, 标签推荐算法

Abstract: 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.

Key words: image labeling system, collaborative filtering algorithm, tag semantic processing, tag recommendation algorithm

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