Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph   Regularization

doi:10.1109/TCSVT.2015.2400779

CORC > 北京大学 > 信息科学技术学院

	Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization
	Peng, Yuxin ; Zhai, Xiaohua ; Zhao, Yunzhen ; Huang, Xin
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2016
关键词	Cross-media retrieval patch graph regularization semi-supervised cross-media feature learning IMAGE RETRIEVAL PROJECTION DOMAIN MODEL
DOI	10.1109/TCSVT.2015.2400779
英文摘要	With the rapid growth of multimedia data such as text, image, video, audio, and 3-D model, cross-media retrieval has become increasingly important, because users can retrieve the results with various types of media by submitting a query of any media type. Comparing with single-media retrieval such as image retrieval and text retrieval, cross-media retrieval is better because it provides the retrieval results with all kinds of media at the same time. In this paper, we focus on how to learn cross-media features for different media types, which is a key challenge for cross-media retrieval. Existing methods either model different media types separately or only exploit the labeled multimedia data. Actually, the data from different media types with the same semantic category are complementary to each other, and jointly modeling them is able to improve the accuracy of cross-media retrieval. In addition, although the labeled data are accurate, they require a lot of human labor and thus are very scarce. To address the above problems, we propose a semi-supervised cross-media feature learning algorithm with unified patch graph regularization (S(2)UPG). Our motivation and contribution mainly lie in the following three aspects. First, existing methods only model different media types in different graphs, while we employ one joint graph to simultaneously model all the media types. The joint graph is able to fully exploit the semantic correlations among various media types, which are complementary to provide the rich hint for cross-media correlation. Second, existing methods only consider the original media instances (such as images, videos, texts, audios, and 3-D models) but ignore their patches, while we make full use of both the media instances and their patches in one graph. Cross-media patches could emphasize the important parts and make cross-media correlations more precise. Third, traditional semi-supervised learning methods only exploit single-media unlabeled instances, while our approach fully exploits cross-media unlabeled instances and their patches, which can increase the diversity of training data and boost the accuracy of cross-media retrieval. Comparing with the current state-of-the-art methods on three datasets, including the challenging XMedia dataset with five media types, the comprehensive experimental results show that our proposed approach performs better.; National Natural Science Foundation of China [61371128]; National Hi-Tech Research and Development Program of China (863 Program) [2014AA015102]; Ph.D. Programs Foundation of Ministry of Education of China [20120001110097]; SCI(E); EI; ARTICLE; pengyuxin@pku.edu.cn; 3; 583-596; 26
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/437614]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Peng, Yuxin,Zhai, Xiaohua,Zhao, Yunzhen,et al. Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2016.
APA	Peng, Yuxin,Zhai, Xiaohua,Zhao, Yunzhen,&Huang, Xin.(2016).Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY.
MLA	Peng, Yuxin,et al."Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2016).