Weakly Supervised Multi-Graph Learning for Robust Image Reranking

CORC > 厦门大学 > 信息技术－已发表论文

	Weakly Supervised Multi-Graph Learning for Robust Image Reranking
	Deng, Cheng ; Ji, Rongrong ; Tao, Dacheng ; Gao, Xinbo ; Li, Xuelong ; Ji RR(纪荣嵘)
刊名	http://dx.doi.org/10.1109/TMM.2014.2298841
	2014
关键词	VISUAL-SEARCH RECOGNITION RANKING MODELS
英文摘要	National Basic Research Program of China (973 Program) [2012CB316400]; National Natural Science Foundation of China [61125106, 61125204, 61101250, 61373076]; Program for New Century Excellent Talents in University [NCET-12-0917]; Program for New Scientific and Technological Star of Shaanxi Province [2012KJXX-24]; Shaanxi Key Innovation Team of Science and Technology [2012KCT-02, 2012KCT-04]; Fundamental Research Funds for the Central Universities [2013121026]; 985 Project of Xiamen University; Visual reranking has been widely deployed to refine the traditional text-based image retrieval. Its current trend is to combine the retrieval results from various visual features to boost reranking precision and scalability. And its prominent challenge is how to effectively exploit the complementary property of different features. Another significant issue raises from the noisy instances, from manual or automatic labels, which makes the exploration of such complementary property difficult. This paper proposes a novel image reranking by introducing a new Co-Regularized MultiGraph Learning (Co-RMGL) framework, in which intra-graph and inter-graph constraints are integrated to simultaneously encode the similarity in a single graph and the consistency across multiple graphs. To deal with the noisy instances, weakly supervised learning via co-occurred visual attribute is utilized to select a set of graph anchors to guide multiple graphs alignment and fusion, and to filter out those pseudo labeling instances to highlight the strength of individual features. After that, a learned edge weighting matrix from a fused graph is used to reorder the retrieval results. We evaluate our approach on four popular image retrieval data sets and demonstrate a significant improvement over state-of-the-art methods.
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92706]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Deng, Cheng,Ji, Rongrong,Tao, Dacheng,et al. Weakly Supervised Multi-Graph Learning for Robust Image Reranking[J]. http://dx.doi.org/10.1109/TMM.2014.2298841,2014.
APA	Deng, Cheng,Ji, Rongrong,Tao, Dacheng,Gao, Xinbo,Li, Xuelong,&纪荣嵘.(2014).Weakly Supervised Multi-Graph Learning for Robust Image Reranking.http://dx.doi.org/10.1109/TMM.2014.2298841.
MLA	Deng, Cheng,et al."Weakly Supervised Multi-Graph Learning for Robust Image Reranking".http://dx.doi.org/10.1109/TMM.2014.2298841 (2014).