标题:Ranking-oriented nearest-neighbor based method for automatic image annotation
作者:Cui, Chaoran ;Ma, Jun ;Lian, Tao ;Wang, Xiaofang ;Ren, Zhaochun
作者机构:[Cui, Chaoran ;Ma, Jun ;Lian, Tao ;Wang, Xiaofang ] School of Computer Science and Technology, Shandong University, Jinan, China;[Ren, Zhaochun ] Inte 更多
会议名称:36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013
会议日期:28 July 2013 through 1 August 2013
来源:SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
出版年:2013
页码:957-960
DOI:10.1145/2484028.2484113
关键词:Image annotation; Learning to rank; Nearest-neighbor based scheme
摘要:Automatic image annotation plays a critical role in keyword-based image retrieval systems. Recently, the nearest-neighbor based scheme has been proposed and achieved good performance for image annotation. Given a new image, the scheme is to first find its most similar neighbors from labeled images, and then propagate the keywords associated with the neighbors to it. Many studies focused on designing a suitable distance metric between images so that all labeled images can be ranked by their distance to the given image. However, higher accuracy in distance prediction does not necessarily lead to better ordering of labeled images. In this paper, we propose a ranking-oriented neighbor search mechanism to rank labeled images directly without going through the intermediate step of distance prediction. In particular, a new learning to rank algorithm is developed, which exploits the implicit preference information of labeled images and underlines the accuracy of the top-ranked results. Experiments on two benchmark datasets demonstrate the effectiveness of our approach for image annotation. Copyright © 2013 ACM.
收录类别:EI;SCOPUS
Scopus被引频次:6
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883097146&doi=10.1145%2f2484028.2484113&partnerID=40&md5=0c6f57e83cfbf4495ce84d4e2946721f
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