中国科学技术大学学报 ›› 2017, Vol. 47 ›› Issue (1): 26-31.DOI: 10.3969/j.issn.0253-2778.2017.01.004

• 原创论文 • 上一篇    下一篇

基于朝向对比度的无监督边界检测算法

张 辉   

  1. 1. 河北大学数学与信息科学学院,河北省机器学习与计算智能重点实验室,河北保定 071002; 2. 河北大学综合实验中心,河北保定 071002
  • 收稿日期:2016-03-01 修回日期:2016-09-17 出版日期:2017-01-31 发布日期:2017-01-31
  • 通讯作者: 赵静
  • 作者简介:张辉,男,1981年生,博士生/讲师. 研究方向:机器学习、数据挖掘. E-mail: zhanghui@hbu.edu.cn
  • 基金资助:
    河北省自然科学基金(F2014201100),河北大学校内人才培养项目资助.

An unsupervised boundary detection algorithm based on orientation contrast model

ZHANG Hui   

  1. 1. College of Mathematics and Information Science, Key Lab. of Machine Learning and Computational Intelligence, Hebei University, Baoding 071102, China; 2. Comprehensive Experimental Center of Hebei University, Baoding 071102, China
  • Received:2016-03-01 Revised:2016-09-17 Online:2017-01-31 Published:2017-01-31

摘要: 针对网络上大规模图像集,没有真实标定边界或者获取成本较大,提出了基于朝向对比度的无监督边界检测算法;在朝向对比度的计算上,考虑了多个方向的差异.特别地,该模型尤其适合于检测自然纹理环绕的对象的边界.在Rug标准数据库上的测试结果表明,提出的算法优于当前最好的无监督边界检测算法,验证了该模型的有效性.

关键词: 边界检测, 朝向对比度, 边缘检测, 谱聚类

Abstract: For large image sets on the Web, due to the absense of a ground truth boundary or the high cost of getting one, an unsupervised boundary detection algorithm based on orientation contrast model was proposed. The model is especially suited for detecting object boundaries surrounded by natural textures. In the Rug image database, the algorithm outperforms the state-of-the-art unsupervised boundary detection algorithm, which verifies the validity of the model.

Key words: boundary detection, orientation contrast, edge detection, spectral clustering