基于深度学习的手绘草图分割算法综述
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通讯作者:

马翠霞,E-mail:cuixia@iscas.ac.cn

基金项目:

国家重点研发计划(2016YFB1001200);国家自然科学基金(61872346)


Sketch Segmentation Algorithm Based on Deep Learning: A Survey
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Fund Project:

National Key Research and Development Project (2016YFB1001200); National Natural Science Foundation of China (61872346)

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    摘要:

    手绘草图一直是人类传递信息的重要工具之一.手绘草图可以通过简单明了的形式更快地表达人类的一些复杂思想,因此对手绘草图的研究也一直是计算机视觉领域的研究热点之一.目前对手绘草图的研究主要集中在识别、检索和补全等方面.随着研究者对于手绘草图细粒度操作的重视,对于手绘草图分割方面的研究也得到越来越多的关注.近年来,随着深度学习与计算机视觉技术的发展,出现了大量基于深度学习的手绘草图分割方法,手绘草图分割的精确度和效率也都得到了较大提升.但是,由于手绘草图自身的抽象性、稀疏性和多样性,手绘草图分割仍然是一个非常具有挑战性的课题.目前,国内很少有关于手绘草图分割的综述.针对这个不足,本文对基于深度学习的手绘草图分割算法进行整理、分类、分析和总结,首先阐述了三种基本的草图表示方法与常用的草图分割数据集,再按草图分割算法的预测结果分别介绍了草图语义分割、草图感知聚类与草图解析算法,然后在主要的数据集上收集与整理草图分割算法的评测结果并对结果进行分析,最后总结了草图分割相关的应用并探讨未来可能的发展方向.

    Abstract:

    Free-hand sketches have always been one of the important tools for human communication. As it can express some complex human thoughts quickly in a succinct form, the study of free-hand sketches has always been one of the research hotspots in the field of computer vision. Currently, the research on free-hand sketches mainly focuses on the recognition, retrieval and completion. As researchers focus on the fine-grained operation of free-hand sketches, research on free-hand sketch segmentation has also received more and more attention. In recent years, with the development of deep learning and computer vision technology, a large number of free-hand sketch segmentation methods based on deep learning have been proposed. Moreover, the accuracy and efficiency of free-hand sketch segmentation have also been significantly increased. However, free-hand sketch segmentation is still a very challenging topic because of the abstraction, sparsity and diversity of free-hand sketches. At present, there are few Chinese reviews on hand-drawn sketch segmentation. This paper organizes, categorizes, analyzes and summarizes the free-hand sketch segmentation algorithm based on deep learning to solve the above deficiency. Firstly, Show three basic sketch representation methods and commonly used sketch segmentation datasets. According to the sketch segmentation algorithm prediction results, introduce sketch semantic segmentation, sketch perceptual grouping and sketch parsing respectively. Moreover, Collect and arrange the evaluation results of sketch segmentation on the primary data sets. Finally, summarize the application of sketch segmentation and discuss the possible future development direction.

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王佳欣,朱志亮,邓小明,马翠霞,王宏安.基于深度学习的手绘草图分割算法综述.软件学报,,():0

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  • 收稿日期:2020-08-07
  • 最后修改日期:2020-09-02
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  • 在线发布日期: 2021-01-15
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