摘要
针对目前图像搜索引擎难以正确把握用户真正意图的问题,从爬虫Web图像搜索引擎检索结果入手,提出三种聚类算法来提取海量Web图像中的语义区域.这三种聚类算法包括确定初始化中心的K-means聚类、确定参数的最大期望聚类以及基于半监督的K-means聚类算法.然后选取显著值较大的显著区域作为语义区域.实验分析比较了三种聚类算法的有效性,最终实现的图像重排系统能比网络搜索引擎更好地反馈给用户精确而且有序的查询结果.
It is difficult for current image search engines to accurately grasp the real intention of users. Based on the search results, we propose three clustering algorithms to extract semantic regions of Web images. These methods include K-means clustering with determined k centers, expectation maximization clustering with the determined parameters, and semi-supervised K-means clustering. We then select the salient regions with the high salient scores as the semantic regions. We demonstrate the experimental results by comparing the three clustering algorithms. The proposed image re-ranking system can more accurately show the ordered search results than web image engines.
出处
《自动化学报》
EI
CSCD
北大核心
2011年第11期1356-1359,共4页
Acta Automatica Sinica
基金
教育部留学回国人员科研启动基金
高等学校博士学科点专项科研基金(20090184120022)
中央高校基本科研业务费专项资金科技创新项目(SWJTU09CX036)资助~~
关键词
语义区域提取
半监督聚类
K-MEANS聚类
最大期望聚类
图像重排
Extraction of semantic regions
semi-supervised clustering
K-means clustering
expectation maximization clustering
image re-ranking
