图像搜索结果的重叠层次聚类与代表点展现

针对图像聚类中面临的高维、准确度低、部分重叠等问题，提出了一种高效的基于链接层次聚类的多标记图像聚类。该方法通过图像距离计算相似度，通过链接聚类检测重叠簇。从而每个图像可能归属于多个簇，使得簇标签的意义更明确。为了检验方法的有效性，对通过搜索引擎检索特定关键词返回的图片数据集进行聚类。结果表明，该方法能有效发现具有重叠划分的簇，且簇的意义比较明确。

Hierarchical overlapping clustering and exemplar visualization of images returned by search engine

To resolve the problems of high dimensionality,low accuracy and overlapping in image clustering,an effective link-clustering based image multiple-cluster partition method was proposed in this paper.This method utilized image distance to measure similarity and identified overlapping clusters by using link-clustering.As a result,an image may be partitioned into multiple clusters,and this multiple-cluster partition makes each cluster more specific compared with others.To validate this method,experiments were carried out on the datasets returned by search engine when searching for some Keywords.The result shows that the proposed method can find explicit clusters with partial overlapping.