摘要
近年来谱聚类算法广泛应用于图像分割领域,而相似度矩阵的构造是谱聚类算法的关键。通常传统的谱聚类算法分割彩色图像时,仅采用一种颜色空间和距离计算公式构造相似度矩阵,而忽略了不同的颜色空间和距离计算公式构造的相似度矩阵对分割结果的影响,导致谱聚类算法有诸多的局限性。针对这个问题,文中分别采用RGB和HSV颜色空间,以及分别在两种颜色空间下使用欧氏距离、余弦距离和卡方距离公式,建立不同的相似度矩阵。分析比较不同构造方法的分割效果,得出了最优分割效果的相似度矩阵构造方法,提高了应用谱聚类算法分割彩色图像的有效性。通过计算性能评价指标查准率和查全率以及分割结果的准确率,验证了实验的可靠性和准确性。
In recent years,spectral clustering algorithm is widely used in the field of image segmentation,and the structure of the similarity matrix is the key of it. When the color images are segmented by traditional spectral clustering algorithm,the only one of color space and distance calculation formula is usually used to construct similarity matrix. The influence of the segmentation results established on the different color space and distance calculation formula is neglected,which leads to many limitations of spectral clustering algorithm. To solve this problem,using the formula of Euclidean distance,cosine distance and chi square distance,the different similarity matrices are established on RGB and HSV color space. The best segmentation construction method of the similarity matrix is obtained by analysis and comparison of the effect of different construction methods. The effectiveness of spectral clustering algorithm for segmenting color images is improved. By calculating the accuracy of image segmentation results and the performance evaluation index of precision and recall,the reliability and accuracy of the experiment are verified.
出处
《计算机技术与发展》
2016年第7期55-58,64,共5页
Computer Technology and Development
基金
国家自然科学基金资助项目(41371425)
辽宁省自然科学基金(2013020048)
关键词
图像分割
谱聚类
相似度矩阵
颜色空间
距离公式
image segmentation
spectral clustering
similarity matrix
color space
distance formula
