摘要
首先采用改进的 k 均值无监督图像分割算法将图像分割成不同的区域,提出信息瓶颈聚类方法对分割后的区域进行聚类,建立图像语义概念和聚类区域之间的相互关系.然后对未标注的图像进行分割,在给出分割区域的条件下,计算每个语义概念的条件概率,使用条件概率最大的语义关键字实现图像语义的自动标注.对一个包含500幅图像的图像库进行实验,结果表明,本文方法比其它方法更有效.
Firstly, a fully unsupervised segmentation algorithm with improved k -means is employed to divide images into regions. Then, a method of information bottleneck is proposed to cluster the segmented region and the relationship between image semantic concept and clustering regions is established. Image segmentation is used in the unannotated image so that the conditional probability of each semantic concept can be calculated under the condition of segmenting region. The image semantics is automatically annotated by keywords with maximal conditional probability. The system is implemented and tested on a 500-image database, and the experimental results show that the effectiveness of the proposed method outperforms others.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第6期812-818,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金重大项目(No.79816101)
湖南省自然科学基金项目(No.05JJ30121)资助
关键词
K均值算法
图像分割
信息瓶颈
图像标注
图像检索
k -Means Method, Image Segmentation, Information Bottleneck, Image Annotation, Image Retrieval
