基于概率随机裁剪的图像缩放算法

郭迎春; 侯骏腾; 于明; 王睿俐

doi:10.11959/j.issn.1000-436x.2017127

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基于概率随机裁剪的图像缩放算法

学术论文 | 更新时间：2024-06-05

- 基于概率随机裁剪的图像缩放算法
- Content-aware image resizing based on random-carving with probability
- 通信学报 2017年38卷第6期页码：30-38
- 作者机构：
  
  1. 河北工业大学计算机科学与软件学院，天津 300401
  2. 梅西大学自然与数学科学系，新西兰奥克兰 4442
- 作者简介：
  
  [ "郭迎春（1970-），女，河北张家口人，河北工业大学副教授，主要研究方向为数字图像处理、图像显著性检测、图像缩放、图像质量评价和人脸识别表情识别等。" ]
  [ "侯骏腾（1990-），男，河北黄骅人，河北工业大学硕士生，主要研究方向为图像处理与模式识别。" ]
  [ "于明（1964-），男，河北秦皇岛人，河北工业大学教授，主要研究方向为语音与图像视觉信息融合的生物特征识别、图像数学变换、图像与视频编码的高效算法和视觉计算及其应用（视频数据挖掘、人脸识别、笔迹鉴别系统及其应用）等。" ]
  [ "王睿俐（1965-），男，新西兰奥克兰人，博士，梅西大学副教授，主要研究方向为模式识别、机器学习和复杂系统等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(60302018);河北省自然科学基金资助项目(F2015202239)
- DOI：10.11959/j.issn.1000-436x.2017127
  中图分类号： TN911.73
- 网络出版日期：2017-06，
  
  纸质出版日期：2017-06-25
- 稿件说明：
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郭迎春, 侯骏腾, 于明, 等. 基于概率随机裁剪的图像缩放算法[J]. 通信学报, 2017,38(6):30-38.

Ying-chun GUO, Jun-teng HOU, Ming YU, et al. Content-aware image resizing based on random-carving with probability[J]. Journal on communications, 2017, 38(6): 30-38.
郭迎春, 侯骏腾, 于明, 等. 基于概率随机裁剪的图像缩放算法[J]. 通信学报, 2017,38(6):30-38. DOI： 10.11959/j.issn.1000-436x.2017127.

Ying-chun GUO, Jun-teng HOU, Ming YU, et al. Content-aware image resizing based on random-carving with probability[J]. Journal on communications, 2017, 38(6): 30-38. DOI： 10.11959/j.issn.1000-436x.2017127.

摘要

为提高图像缩放的速度，提出一种结合阈值学习与依概率随机裁剪的快速内容感知图像缩放算法，通过计算图像的重要度图，利用径向基函数（RBF

radial basis function）神经网络进行阈值学习求出图像的重要度阈值，根据阈值将图像分成保护区域和非保护区域，并按缩放要求为其分配不同的缩放比，分别进行依概率随机裁剪。在MSRA图像数据库上与目前流行的内容感知缩放方法进行对比，实验结果表明，所提方法的缩放时间明显低于其他算法，而且在缩放效果上有明显的优势。

Abstract

To improve the running speed of image resizing

a fast content-aware image resizing algorithm was proposed based on the threshold learning and random-carving with probability.Firstly the important map was calculated by combining the graph-based visual saliency map and gradient map.Then the image threshold value was obtained by radial basis function (RBF) neural network learning.And by the threshold

the original image was separated into the protected part and the unprotected part which was corresponding to the important part and the unimportant part of the original image individually.Finally

the two parts were allocated different resizing scales and the random-carving with probability was applied to them respectively.Experiments results show that the proposed algorithm has lower time cost comparing to the state-of-arts algorithms in MSRA image database

and has a better visual perception on image resizing.

关键词

Keywords

references

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