摘要
针对颜色密度聚类分割模型容易产生误分割的问题,提出基于视觉显著性调节的主颜色聚类分割算法.首先,根据空间颜色信息和Mean-shift算法平滑结果分别计算图像的全局显著特征和区域显著特征,并融合2类显著特征作为特征空间聚类的约束项.然后,采用核密度估计方法计算图像主颜色作为初始类,并将显著特征作为调节因子进行聚类分割.最后,进行区域合并.在标准的分割图像库上进行实验并与多种算法对比,结果表明,文中算法具有更高的区域轮廓准确度,并且有效利用图像显著特征,降低密度聚类形成的区域不一致性,提高像素聚类的精度和分割的鲁棒性.
Aiming at the fault segmentation caused by color density clustering segmentation model, a dominant colors clustering image segmentation algorithm is proposed based on visual saliency. Firstly, according to the spatial color information and Mean-shift smoothing results, the global saliency and region saliency of the image are computed and fused as the constraints of spatial clustering. Then, kernel density estimation is employed to compute dominant colors of image as initial clusters and the salient features are taken as regulated factors for clustering segmentation. Finally, regions are merged for final segmentation. The experiments are implemented on the standard segmentation database and the proposed algorithm is compared with several algorithms. The experimental results show the higher precision of the proposed algorithm on region contours. The proposed algorithm makes good use of the salient feature of image,reduces the inconsistency of the clustering results, and improves the accuracy of pixel clustering and the robustness of the segmentation.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2016年第6期492-503,共12页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金重点项目(No.U1261206)
国家自然科学基金项目(No.61572173)
河南省科技计划项目(No.162102210062)
河南省教育厅高等学校重点科研项目(No.15A520072)
河南理工大学博士基金项目(No.B2016-37)资助~~
关键词
彩色图像分割
显著特征
显著性融合
主颜色聚类
Color Image Segmentation, Salient Feature, Saliency Fusion, Dominant Colors Clustering
