基于改进颜色直方图和灰度共生矩阵的图像检索  

Image Retrieval Based on Improved Color Histogram and Gray Level Co-occurrence Matrix

作　　者：吴庆涛[1,2] 曹再辉[1] 施进发[1] WU Qingtao;CAO Zaihui;SHI Jinfa(Zhengzhou University of Aeronautics, Zhengzhou Henan 450015 China;Collaborative Innovation Center for Aviation Economy Development, Zhengzhou Henan 450015 China)

机构地区：[1]郑州航空工业管理学院,河南郑州450015 [2]航空经济发展河南省协同创新中心,河南郑州450015

出　　处：《图学学报》2017年第4期543-548,共6页Journal of Graphics

基　　金：国家自然科学基金项目(71371172);河南省高等学校重点科研项目(15A520105);2017年度河南省科技攻关项目(172102210523)

摘　　要：针对传统颜色直方图提取的颜色特征维数高、传统灰度共生矩阵忽视纹理方向等问题,提出一种融合改进的颜色直方图和灰度共生矩阵算法的新图像检索算法。利用K-means聚类对检测图像进行颜色聚类以降低图像颜色数;在HSV空间进行矢量化编码,统计图像码字形成颜色直方图以提取颜色特征;利用灰度共生矩阵提取检测图像的4个特征值,利用方向测度引入权值因子,将其与4个特征值融合,对融合后的各分量进行高斯归一化后形成纹理特征向量;最后,采用加权平均融合颜色和纹理的特征距离。与其他两种算法相比,仿真实验表明本算法对一般图像和有纹理倾向的图像有较高的查全率和查准率。There are the problems that the extracted color feature is high dimension based on the traditional color histogram,and the direction of texture is neglected based on the traditional co-occurrence matrix.A new image retrieval algorithm combining the improved color histogram and gray level co-occurrence matrix algorithm is proposed.The K-means clustering is used to cluster the detected images in order to reduce the number of colors.The image codes are computed to form the color histogram based on vector codes in the HSV space.So the color features are extracted.The gray level co-occurrence matrix is used to extract the four eigenvalues of the detected image,and the four eigenvalues are combined with the weighting factor determined by the direction measure.The texture eigenvectors are obtained from normalizing the fused components.Finally,weighted average is used to fuse the feature distance of color and texture.Compared with the other two algorithms,experimental results show that our algorithm has higher recall and precision in general images and textured images.

关 键 词：图像检索 颜色直方图 颜色聚类 矢量化编码 灰度共生矩阵 方向测度 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]