摘要
通过构建二维压缩感知测量模型,提出一种分层HSV特征和分层纹理特征提取与图像检索新算法。在图像HSV空间上引入网格离散划分和分层映射算子,定义一种基于HSV网格空间上的分层映射矩阵和拟灰度共生矩阵;设计归一化Gauss随机矩阵作为测量矩阵,使用二维压缩感知测量模型对上述两种矩阵进行压缩采样;采用PCA(Principal Component Analysis)方法获取上述两种分层采样矩阵的特征值序列,作为图像的分层HSV特征与分层纹理特征。最后融合上述两类特征综合计算图像间的整体相似度并实现图像检索。仿真实验表明,上述两类特征具有很好的可区分性,有效提高了图像检索效率。
By constructing a two-dimensional（ 2D） compressive sensing（ CS） measurement model,a new image retrieval algorithm is proposed by extracting hierarchical HSV features and texture features.Firstly,the ideas of grid discrete partition and hierarchical mapping in HSV space are introduced,and hierarchical mapping matrix and similar GLCM in HSV grid space are defined. Secondly,the normalized Gauss random matrix is designed as measurement matrix,and compressive sampling on the above two matrixes is performed by 2D CS measurement model. With using PCA（ Principal Component Analysis）,the feature sequences as hierarchical HSV features and texture features are obtained from the above two hierarchical sampling matrixes. Finally,the above two features are infused to compute the overall similarity among images. Experimental results show that the above two features have good discrimination. It can improve the efficiency for image retrieval,and has good performance especially for images with complex backgrounds.
出处
《中山大学学报（自然科学版）》
CAS
CSCD
北大核心
2016年第3期77-82,共6页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
广东省自然科学基金资助项目(2015A030313635
2015A030313672
2016A030311013)
广东省科技计划资助项目(2014A010103037)
广东省教育厅省级重大资助项目(2014KZDXM060
2015KGJHZ021)
广东省教育厅特色创新类资助项目(2015KTSCX153)
佛山市科技创新专项资金资助项目(2015AG10008
2014AG10001)
佛山科学技术学院优秀青年教师培养计划资助项目(fsyq201411)
关键词
二维压缩感知
分层纹理特征
分层HSV特征
拟灰度共生矩阵
two-dimensional compressive sensing
hierarchical texture feature
hierarchical HSV feature
similar GLCM
