Non-uniform Mesh Warping for Content-Aware Image Retargeting

被引:0
|
作者
Bao, Huiyun [1 ]
Li, Xueqing [1 ]
机构
[1] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China
关键词
Image retargeting; sampling mesh vertices; non-uniform mesh warping; patch-linking scheme;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image retargeting is the process of adapting an existing image to display with arbitrary sizes and aspect ratios. A compelling retargeting method aims at preserving the viewers' experience by maintaining the significant regions in the image. In this paper, we present a novel image retargeting method based on non-uniform mesh warping, which can effectively preserve both the significant regions and the global configuration of the image. The main idea of our method is sampling mesh vertices based on the saliency map, that is to say, we place mesh vertices more densely in the significant regions, defining different quadratic error metrics to measure image distortion and adopting a patch-linking scheme that can better preserve the global visual effect of the entire image. Moreover, to increase efficiency, we formulate the image retargeting as a quadratic minimization problem carried out by solving linear systems. Our experimental results verify its effectiveness.
引用
收藏
页码:262 / 273
页数:12
相关论文
共 50 条
  • [31] Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
    Cho, Donghyeon
    Park, Jinsun
    Oh, Tae-Hyun
    Tai, Yu-Wing
    Kweon, In So
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, : 4568 - 4577
  • [32] A Content-Aware Image Retargeting Quality Assessment Method Using Foreground and Global Measurement
    Li, Yuwei
    Guo, Lihua
    Jin, Lianwen
    IEEE ACCESS, 2019, 7 : 91912 - 91923
  • [33] A Quad Edge-Based Grid Encoding Model for Content-Aware Image Retargeting
    Kim, YoonHyung
    Eun, Hyunjun
    Jung, Chanho
    Kim, Changick
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2019, 25 (12) : 3202 - 3215
  • [34] Smart Scaling: A Hybrid Deep-Learning Approach to Content-Aware Image Retargeting
    Dickman, Elliot
    Diefenbach, Paul
    Burlick, Matthew
    Stockton, Mark
    PROCEEDINGS OF SIGGRAPH 2023 POSTERS, SIGGRAPH 2023, 2023,
  • [35] A Content-Aware Image Prior
    Cho, Taeg Sang
    Joshi, Neel
    Zitnick, C. Lawrence
    Kang, Sing Bing
    Szeliski, Richard
    Freeman, William T.
    2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, : 169 - 176
  • [36] Rectangling and enhancing underwater stitched image via content-aware warping and perception balancing
    Chang, Laibin
    Wang, Yunke
    Du, Bo
    Xu, Chang
    NEURAL NETWORKS, 2025, 181
  • [37] Low Complexity Content-Aware Video Retargeting for Mobile Devices
    Nam, Hyeong-Min
    Byun, Keun-Yung
    Jeong, Jae-Yun
    Choi, Kang-Sun
    Ko, Sung-Jea
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2010, 56 (01) : 182 - 189
  • [38] Directorial Editing: A Hybrid Deep-Learning Approach to Content-Aware Image Retargeting and Resizing
    Dickman, Elliot
    Diefenbach, Paul
    ELECTRONICS, 2024, 13 (22)
  • [39] A Comprehensive Review on Content-Aware Image Retargeting: From Classical to State-of-the-art Methods
    Asheghi, Bahareh
    Salehpour, Pedram
    Khiavi, Abdolhamid Moallemi
    Hashemzadeh, Mahdi
    SIGNAL PROCESSING, 2022, 195
  • [40] Fast Content Aware Image Retargeting
    Srivastava, Awadhesh
    Biswas, K. K.
    SIXTH INDIAN CONFERENCE ON COMPUTER VISION, GRAPHICS & IMAGE PROCESSING ICVGIP 2008, 2008, : 505 - 511