基于边缘特征学习的自然图像对称轴检测

doi:10.3969/j.issn.1007-2861.2014.03.012

上海大学学报(自然科学版) ›› 2014, Vol. 20 ›› Issue (6): 715-725.doi: 10.3969/j.issn.1007-2861.2014.03.012

基于边缘特征学习的自然图像对称轴检测

沈为1, 2, 程小青1, 2, 曾丹1, 2

1. 上海大学通信与信息工程学院, 上海200444;2. 上海大学特种光纤与光接入网省部共建重点实验室, 上海200072

收稿日期:2014-08-14 出版日期:2014-12-23 发布日期:2014-12-23
通讯作者: 沈为(1985—), 男, 讲师, 博士, 研究方向为计算机视觉和模式识别. E-mail:wei.shen@shu.edu.cn
作者简介:沈为(1985—), 男, 讲师, 博士, 研究方向为计算机视觉和模式识别. E-mail: wei.shen@shu.edu.cn
基金资助:
国家自然科学基金资助项目(61303095); 高等学校博士学科点专项科研基金资助项目(20133108120017);上海高校青年教师培养资助计划(ZZSD13005)

Symmetry Detection in Natural Images via Edge Feature Learning

SHEN Wei-1, 2 , CHENG Xiao-Jing-1, 2 , ZENG Dan-1, 2

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;2. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai 200072, China

Received:2014-08-14 Online:2014-12-23 Published:2014-12-23

摘要/Abstract

摘要： 主要研究计算机视觉中一个非常具有挑战性的问题——自然图像中对称轴的检测. 由于图像中杂乱的场景和物体形态的变化, 使得在自然图像中判断像素是否处于对称轴上是非常困难的. 为了解决这个难题, 考虑到边缘与对称轴的互补关系, 提出了2种边缘特征用于帮助对称轴的检测. 2种特征都定义在成对的边缘上, 分别是为了找到边缘强度高和到对称轴距离相等的成对的边缘. 在多尺度和多角度上提取这2种边缘特征, 把它们与底层描述子(颜色、亮度、纹理等)差分特征结合在一起, 在多示例学习的框架下检测自然图像中的对称轴.在SYMMAX300数据集上的实验结果证明了2种边缘特征能够提升对称轴检测的性能.

关键词: 边缘, 底层特征, 对称轴, 对称轴检测, 多示例学习

Abstract: This paper studies symmetry detection in natural images, which is a challenging problem in computer vision. To differentiate symmetry and non-symmetry in natural images is intractable due to the large variation in objects and the cluttered scene. To address this problem, two types of edge features motivated by the fact that symmetries are complementary to edges are proposed. These two types of features are both defined on pairs of edges to search pairs of edges with consistent high strength and equal distances to symmetries, respectively. The proposed edge features at multiple scales and orientations
and integrate them with low level cues (color, brightness and texture) under a multiple instance learning framework to detect symmetries are extracted. The experimental results on SYMMAX300 dataset demonstrate that both proposed edge features can improve performance of symmetry detection.

Key words: edge, low level feature, multiple instance learning, symmetry axis, symmetry detection

中图分类号:

TP 751.1

沈为1, 2, 程小青1, 2, 曾丹1, 2. 基于边缘特征学习的自然图像对称轴检测[J]. 上海大学学报(自然科学版), 2014, 20(6): 715-725.

SHEN Wei-1, 2 , CHENG Xiao-Jing-1, 2 , ZENG Dan-1, 2 . Symmetry Detection in Natural Images via Edge Feature Learning[J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(6): 715-725.

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基于边缘特征学习的自然图像对称轴检测

Symmetry Detection in Natural Images via Edge Feature Learning

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