自动化学报Issue(4)：706-712,7.DOI:10.3724/SP.J.1004.2014.00706
大规模图像集中的代表性图像选取
Representative Image Selection from Image Dataset
摘要
Abstract
In a traditional image retrieval system, people search images using keywords. However, the result shows a lack of diversity in the sense of semantic theme. For the problem, we propose a viable method for representative image selection. We define representative images as those with diverse contents in the semantic meaning to cover different semantic forms of a query. First, we use mutual nearest neighbor consistency to adjust the similarity between images as the input to the AP clustering. Then we select representative clusters based on cluster ranking and finally take the images of the cluster center from representative clusters as a summary of the image dataset. The results showed that the performance of our method is better than the K-means based method and the greedy K-means based method. The selected images can summarize the content of the original image dataset intuitively and effectively, and they are diverse in semantic meaning as well.关键词
代表性图像/语义主题/互近邻一致性/AP聚类/图像簇排名Key words
Representative images/semantic theme/AP clustering/mutual nearest neighbor consistency/cluster ranking引用本文复制引用
齐美彬,朱俊俊,纪平,蒋建国..大规模图像集中的代表性图像选取[J].自动化学报,2014,(4):706-712,7.基金项目
国家自然科学基金(61172164)资助@@@@Supported by National Natural Science Foundation of China (61172164) （61172164）
