欢迎访问《图学学报》 分享到：
图学学报

图学学报

• 视觉与图像 • 上一篇    下一篇

基于改进颜色直方图和灰度共生矩阵的图像检索

  

  1. 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
  • 出版日期:2017-08-31 发布日期:2017-08-10
  • 基金资助:
    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)

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

  1. 1. Zhengzhou University of Aeronautics, Zhengzhou Henan 450015 China;
    2. Collaborative Innovation Center for Aviation Economy Development, Zhengzhou Henan 450015 China
  • Online:2017-08-31 Published:2017-08-10

PDF (PC)

401

被引次数

3

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。

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

Abstract: 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.

Key words: image retrieval, color histogram, color clustering, vector coding, gray level co-occurrence
matrix, direction measurement