基于颜色不变特征的谱聚类双分图分割方法

doi:10.3969/j.issn.1671-1122.2018.12.002

信息网络安全 ›› 2018, Vol. 18 ›› Issue (12): 8-14.doi: 10.3969/j.issn.1671-1122.2018.12.002

基于颜色不变特征的谱聚类双分图分割方法

赵薇^1,^2,³, 赵娜¹(), 张怡兴⁴

1. 国防科技大学计算机学院,湖南长沙410073
2. 湖南警察学院信息技术（网监）系,湖南长沙410073
3. 网络犯罪侦查湖南省普通高等学校重点实验室,湖南长沙410073
4. 珠海市公安局网警支队香洲大队,广东珠海 519000

收稿日期:2018-09-25 出版日期:2018-12-20 发布日期:2020-05-11
作者简介:
作者简介：赵薇（1982—）,女,湖南,副教授,博士研究生,主要研究方向为数字图像取证、网络空间安全;赵娜（1988—）,女,辽宁,助理教授,博士研究生,主要研究方向为网络空间安全;张怡兴（1975—）,男,广东,本科,主要研究方向为网络安全、网络侦查。
基金资助:
国家自然科学基金[61471169];湖南省科技重大专项[2017SK1040];湖南省教育厅创新开放基金[16K028]

Spectral Clustering Bipartite Graph Segmentation Method Based on Color Invariant Features

Wei ZHAO^1,^2,³, Na ZHAO¹(), Yixing ZHANG⁴

1. College of Computer, National University of Defense Technology, Changsha Hunan 410073, China
2.Department of Information Technology, Hunan Police Academy, Changsha Hunan 410073, China
3.Key Laboratory of Network Crime Investigation, Colleges of Human Province, Changsha Hunan 410073,China
4.Xiangzhou Brigade, Network Police Detachment of Zhuhai Public Security Bureau, Zhuhai Guangdong 519000,China

Received:2018-09-25 Online:2018-12-20 Published:2020-05-11

摘要/Abstract

摘要：

谱聚类分割方法的结果很大程度上受超像素分类聚合效果的影响,而超像素分类聚合的效果关键在于超像素之间的相似性模型。基于双分图的分割框架利用交叉相似性矩阵可以高效完成超像素分类聚合,但其相似性模型采用简单的颜色特征,对强光照射、遮蔽等光照变化不具有鲁棒性,影响目标分割的精度。为了提高超像素聚合的一致性,文章提出利用具有颜色不变特征的颜色描述子和能够反映物理表面反射变化的Ridge特征来构建交叉相似性模型。在Berkeley分割数据集中的实验验证,基于颜色不变特征的谱聚类分割方法获得了比已有分割算法更好的效果。

关键词: 谱聚类, 超像素, 双分图分割, 颜色不变性

Abstract:

The segmentation results of segmentation methods based on spectral clustering are significantly affected by the performance of superpixels clustering. However, the performance of superpixels clustering mostly depends on the construction of affinity model. Bipartite graph segmentation framework with cross-affinity matrix makes superipixels clustering more efficient, but its affinity model uses simple color features without considering the effect of illuminant changes such as highlights, shading et al, which may result in failed object segmentation. To improve the coherence of superpixels clustering, this paper uses color descriptor with color invariant features and Ridge feature which reflects physical reflection of imaging surface to construct cross-affinity model. Based on the validation in Berkeley database, the spectral clustering segmentation method based on color invariant features achieves better performances compared to existing segmentation techniques.

Key words: spectral clustering, superpixels, bipartite graph segmentation, color invariance

中图分类号:

TP309

赵薇, 赵娜, 张怡兴. 基于颜色不变特征的谱聚类双分图分割方法[J]. 信息网络安全, 2018, 18(12): 8-14.

Wei ZHAO, Na ZHAO, Yixing ZHANG. Spectral Clustering Bipartite Graph Segmentation Method Based on Color Invariant Features[J]. Netinfo Security, 2018, 18(12): 8-14.

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评估指标算法	PRI	GCE
FH	0.7139	0.1746
FNcut	0.8146	0.1809
MNcut	0.7559	0.1925
SAS	0.8266	0.1868
本文方法	0.8315	0.1754

基于颜色不变特征的谱聚类双分图分割方法

Spectral Clustering Bipartite Graph Segmentation Method Based on Color Invariant Features

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