计算机工程与应用2017，Vol.53Issue(2)：195-200,270,7.DOI:10.3778/j.issn.1002-8331.1504-0287
特征融合与objectness加强的显著目标检测
Feature fusing and objectness enhanced approach of saliency detection
摘要
Abstract
Saliency detection is a fundamental part of computer vision applications, and the goal is to detect important pixels or regions in an image which attracts human visual attention most. By analyzing some recent methods, a new approach is proposed to solve detection errors problems and to enhance the adaptation of features in saliency detection. It detects saliency in the perspective of both object and background and integrates multi features. It extracts color distinctness feature in the perspective of object and extracts boundary prior feature in the perspective of background, and then combines the two features to obtain the corresponding map. In order to keep accuracy, it uses objectness feature to refine the saliency of detected regions. In comparison experiments, it achieves an average precision of 92.4% on MSRA-1000 databases, and achieves higher precision on CSSD dataset and ECSSD dataset. Experimental results demonstrate the used features make up for each other, which can enhance the saliency detection accuracy.关键词
计算机视觉/显著目标检测/边界先验/颜色区别性/objectnessKey words
computer vision/saliency detection/boundary prior/color distinctness/objectness分类
信息技术与安全科学引用本文复制引用
王娇娇,刘政怡,李辉..特征融合与objectness加强的显著目标检测[J].计算机工程与应用,2017,53(2):195-200,270,7.基金项目
高等学校博士学科点专项科研基金联合资助课题（No.20133401110009）；安徽高校省级自然科学研究项目（No.KJ2015A009）。 （）
