基于视觉与标注相关信息的图像聚类算法

算法首先按视觉相关程度对标注字进行打分,标注字的分值体现了语义一致图像的视觉连贯程度.利用图像语义类别固有的语言描述性,从图像标注中抽取具有明显视觉连贯性的标注字作为图像的语义类别,减少了数据库设计者繁琐的手工编目工作.按标注字信息对图像进行语义分类,提高了图像聚类的语义一致性.对4500幅Corel标注图像的聚类结果证实了算法的有效性.

Image Clustering Based on Correlation Between Visual Features and Annotations

The paper proposes an unsupervised semantic categorization algorithm for annotated images. In order to establish image categories automatically by unsupervised learning ,the algorithm first scores the annotation words for each image by using their relevance to visual features. The scores of annotation words indicate to what extent these words have visual characteristics. The words with a good visually discriminative power can be chosen as image categories. Then a recursive clustering algorithm is presented to group images into the extracted semantic categories according to their annotation. Experiments using a 4500-image Corel database show the efficacy of the proposed algorithm. The results can be exploited for better image browsing and image retrieval.