计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 84-91.doi: 10.11896/jsjkx.190900006

• 计算机图形学&多媒体 • 上一篇    下一篇

视觉图像显著性检测综述

袁野1,2,3, 和晓歌1, 朱定坤4, 王富利4, 谢浩然5, 汪俊1, 魏明强1,2,3, 郭延文3   

  1. 1 南京航空航天大学计算机科学与技术学院/人工智能学院 南京210006
    2 模式分析与机器智能工业和信息化部重点实验室 南京210016
    3 南京大学计算机软件新技术国家重点实验室 南京210023
    4 香港公开大学 香港999077
    5 香港教育大学 香港999077
  • 收稿日期:2019-09-02 出版日期:2020-07-15 发布日期:2020-07-16
  • 通讯作者: 魏明强(mqwei@nuaa.edu.cn)
  • 作者简介:yuenye@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金项目 (61502137);中央高校基本科研业务费 (NJ2019010);南京大学计算机软件新技术国家重点实验室开题课题 (KFKT2018B20);香港教育大学2018-19院长研究基金之早期职业计划种子基金 (SFG-10);香港教育大学2018-19院长研究基金之跨学科研究计划基金 (FLASS/DRF/IDS-3)

Survey of Visual Image Saliency Detection

YUAN Ye1,2,3, HE Xiao-ge1, ZHU Ding-kun4, WANG Fu-lee4, XIE Hao-ran5, WANG Jun1, WEI Ming-qiang1,2,3, GUO Yan-wen3   

  1. 1 College of Computer Science and Technology/College of Artificial Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 210006,China
    2 MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing 210016,China
    3 State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China
    4 The Open University of Hong Kong,Hong Kong 999077,China
    5 The Education University of Hong Kong,Hong Kong 999077,China
  • Received:2019-09-02 Online:2020-07-15 Published:2020-07-16
  • About author:YUAN Ye,born in 1998,postgraduate.His main research interests include computer vision and so on.
    WEI Ming-qiang,born in 1985,Ph.D,associate professor.His main research interest is computer graphics with an emphasis on smart geometry proces-sing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61502137),Fundamental Research Funds for the Central Universities (NJ2019010),Grant from State Key Laboratory for Novel Software Technology at Nanjing University (KFKT2018B20),Seed Fund for Early Career Scheme of the Dean’s Research Fund 2018-19 (SFG-10) of the Education University of Hong Kong and Interdisciplinary Research Scheme of the Dean’s Research Fund 2018-19 (FLASS/DRF/IDS-3) of the Education University of Hong Kong

摘要: 当今图像数据呈爆炸式增长,如何利用计算机高效地获取、处理图片信息成为领域内重要的研究课题。在人类视觉注意机制的启发下,研究人员发现将这种机制引入机器图像处理任务中可以大大提高信息提取的效率,从而更好地节省有限的计算资源。视觉图像显著性检测即利用计算机模拟人类的视觉注意机制,对图片中各部分信息的重要程度进行计算。其在图像分割、视频压缩、目标检测、图像索引等领域得到了广泛的应用,有着重要的研究价值。文中介绍了图像显著性检测算法的研究现状,首先以信息驱动来源为切入点,对显著性检测模型进行概述,之后分析了现有几种典型的显著性检测算法,并根据是否基于学习的模型将其分为基于非学习模型、基于传统机器学习模型以及基于深度学习模型3类。针对第一类,文中较为详细地对基于局部对比度和基于全局对比度的显著性检测算法进行了分类比较,指出了各自的优势与不足;针对后两类,分析了机器学习算法及深度学习在显著性检测中的应用。最后对现有的显著性检测算法进行了总结比较,对该领域研究的下一步发展方向进行了展望。

关键词: 显著区域检测, 视觉显著性检测, 深度学习, 机器学习, 视觉注意机制

Abstract: In today’s society where image data are exploding,how to use computer to efficiently acquire and process image information has become an important research topic.Under the inspiration of human visual attention mechanism,researchers have found that when this mechanism is introduced into machine image processing tasks,the efficiency of information extraction can be greatly improved,thus saving more limited computing resources.Visual image saliency detection is to use computers to simulate the human visual attention mechanism to calculate the importance of the information of each part in the image,which has been widely used in image segmentation,video compression,target detection,image indexing and other aspects,and has important research values.This paper summarizes and introduces the research situation of image saliency detection algorithms.Firstly,it takes information-driven sources as starting point to summarize the saliency detection model,and then analyzes several typical saliency detection algorithms.The models are divided into 3 categories according to whether they are based on learning models,which are based on non-learning models,based on traditional machine learning models and based on deep learning models.For the first category,the paper compares in more details the saliency detection algorithms based on local contrast and global contrast,and points out their respective advantages and disadvantages.For the latter two categories,this paper analyzes the application of machine learning algorithms and deep learning in saliency detection.Finally,this paper summarizes and compares the existing saliency detection algorithms and prospects the future development direction of the research in this aspect.

Key words: Salientregion detection, Visual saliency detection, Deep learning, Machine learning, Visual attention mechanism

中图分类号: 

  • TP391
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