大规模图像集中的代表性图像选取

齐美彬; 朱俊俊; 纪平; 蒋建国

doi:10.3724/SP.J.1004.2014.00706

大规模图像集中的代表性图像选取

doi: 10.3724/SP.J.1004.2014.00706

1.
合肥工业大学计算机与信息学院合肥 230009;
2.
合肥学院电子信息与工程系合肥 230022

基金项目:

国家自然科学基金（61172164）资助

详细信息

作者简介:
齐美彬合肥工业大学计算机与信息学院教授.主要研究方向为视频编码，运动目标检测与跟踪和DSP 技术.E-mail：qimeibin@163.com

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- 文章访问数: 1426
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- 被引次数: 0
出版历程
- 收稿日期: 2012-11-26
- 修回日期: 2013-03-04
- 刊出日期: 2014-04-20

Representative Image Selection from Image Dataset

1.
School of Computer and Information, Hefei University of Technology, Hefei 230009;
2.
Department of Electronics & Electrical Engineering, Hefei University, Hefei 230022

Funds:

Supported by National Natural Science Foundation of China (61172164)

摘要

摘要: 针对传统图像检索系统通过关键字搜索图像时缺乏语义主题多样性的问题，提出了一种基于互近邻一致性和近邻传播的代表性图像选取算法，为每个查询选取与其相关的不同语义主题的图像集合. 该算法利用互近邻一致性调整图像间的相似度，再进行近邻传播（AP）聚类将图像集分为若干簇，最后通过簇排序选取代表性图像簇并从中选取中心图像为代表性图像. 实验表明，本文方法的性能超过基于K-means的方法和基于Greedy K-means的方法，所选图像能直观有效地概括源图像集的内容，并且在语义上多样化.
- 代表性图像 /
- 语义主题 /
- 互近邻一致性 /
- AP聚类 /
- 图像簇排名
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.
- Representative images /
- semantic theme /
- AP clustering /
- mutual nearest neighbor consistency /
- cluster ranking

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