应用图像局部特征和全局特征对比的显著性检测模型  

Saliency Detection Model Using Contrast of Local Feature and Global Feature in Image

作　　者：朱玲[1] 徐蔚鸿[1,2] 陈沅涛[1] 张波[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410114 [2]南京理工大学计算机科学与工程学院,南京210094

出　　处：《小型微型计算机系统》2016年第9期2109-2113,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金青年项目(61402053)资助;湖南省科技计划项目(2014SK3080)资助;湖南省教育厅优秀青年项目(14B005)资助

摘　　要：针对现有显著性检测模型准确度不高的问题,提出一种应用局部特征和全局特征对比的显著性检测模型.该算法首先使用简单的线性迭代聚类(Simple Linear Iterative Clustering,SLIC)分割算法将图像预分割为若干紧凑的超像素,选取边界区域集并计算所有超像素的边界权重;然后计算颜色和纹理特征的局部对比度得到局部显著图,利用全局特征的独特性,空间分布特性得到全局显著图;最后采用求和乘积(Sum and Product,SP)方法将局部和全局显著图融合得到最终的显著图.在Achanta测试集上进行对比分析,实验结果表明本文算法能更准确地检测出显著区域,与其它5种算法相比具有较大的优势.Considering the low accuracy of existing saliency detection model, this paper presents a model which uses local feature and global feature contrast to extract saliency area. First, the algorithm uses SLIC （ Simple Linear Iterative Clustering ） segmentation meth- od to segment the image into several compact super pixels and select the boundary area set to calculate the boundary weighting param- eter for each super pixel. Then the local saliency map is obtained by calculating the local contrast of color and texture features and the global saliency map is obtained by using the global feature uniqueness and spatial distribution characteristics. Finally, a new method SP （Sum and Product） is designed to integrate the local and global saliency map to get the final saliency map. The experimental result on the Achanta database demonstrates that the proposed algorithm outperforms than other 5 visual saliency detection methods in terms of accuracy.

关 键 词：显著性检测 局部对比 全局对比 超像素 显著图 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]