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| 基于内容的图像分割方法综述 |
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姜枫1,2,3, 顾庆1,2, 郝慧珍1,2,4, 李娜1,2, 郭延文1,2, 陈道蓄1,2
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1.计算机软件新技术国家重点实验室(南京大学), 江苏 南京 210023;2.南京大学 计算机科学与技术系, 江苏 南京 210023;3.南京理工大学泰州科技学院 移动互联网学院, 江苏 泰州 225300;4.南京工程学院 通信工程系, 江苏 南京 211167
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| 摘要: |
| 图像分割是指将图像分成若干具有相似性质的区域的过程，是许多图像处理任务的预处理步骤.近年来，国内外学者主要研究基于图像内容的分割算法.在广泛调研大量文献和最新成果的基础上，将图像分割算法分为基于图论的方法、基于像素聚类的方法和语义分割方法这3种类型并分别加以介绍.对每类方法所包含的典型算法，尤其是最近几年利用深度网络技术的语义图像分割方法的基本思想、优缺点进行了分析、对比和总结.介绍了图像分割常用的基准数据集和算法评价标准，并用实验对各种图像分割算法进行对比.最后进行总结，并对未来可能的发展趋势加以展望. |
| 关键词: 图像分割 图论 聚类 语义分割 深度神经网络 |
| DOI：10.13328/j.cnki.jos.005136 |
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| 基金项目:国家自然科学基金（61373012，91218302，61321491，61373059）；江苏省高校自然科学研究项目（15KJB520016）；江苏省自然科学基金（BK20150016） |
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| Survey on Content-Based Image Segmentation Methods |
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JIANG Feng1,2,3, GU Qing1,2, HAO Hui-Zhen1,2,4, Li Na1,2, GUO Yan-Wen1,2, CHEN Dao-Xu1,2
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1.State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210023, China;2.Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;3.College of Mobile Internet, Taizhou Institute of Science & Technology, Nanjing University of Science and Technology, Taizhou 225300, China;4.Department of Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China
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| Abstract: |
| Image segmentation is the process of dividing the image into a number of regions with similar properties, and it's the preprocessing step for many image processing tasks. In recent years, domestic and foreign scholars mainly focus on the content-based image segmentation algorithms. Based on extensive research on the existing literatures and the latest achievements, this paper categorizes image segmentation algorithms into three types:graph theory based method, pixel clustering based method and semantic segmentation method. The basic ideas, advantage and disadvantage of typical algorithms belong to each category, especially the most recent image semantic segmentation algorithms based on deep neural network are analyzed, compared and summarized. Furthermore, the paper introduces the datasets which are commonly used as benchmark in image segmentation and evaluation criteria for algorithms, and compares several image segmentation algorithms with experiments as well. Finally, some potential future research work is discussed. |
| Key words: image segmentation graph theory clustering semantic segmentation deep neural network |
