Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation

CORC > 厦门大学 > 信息技术－已发表论文

	Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation
	Zhang, Luming ; Gao, Yue ; Xia, Yingjie ; Lu, Ke ; Shen, Jialie ; Ji, Rongrong ; Ji RR(纪荣嵘)
刊名	http://dx.doi.org/10.1109/TMM.2013.2293424
	2014
关键词	RETRIEVAL RECOGNITION
英文摘要	Singapore National Research Foundation under its International Research Centre@Singapore Funding Initiative; Nature Science Foundation of China [61373076]; Fundamental Research Funds for the Central Universities [2013121026]; 985 Project of Xiamen University; Natural Science Foundation of China [61002009]; Key Science and Technology Program of Zhejiang Province of China [2012C01035-1]; Zhejiang Provincial Natural Science Foundation of China [LZ13F020004]; Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by leveraging its image-level semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel sets from image-level labels. More specifically, we first extract graphlets from a given image, which are small-sized graphs consisting of superpixels and encapsulating their spatial structure. Then, an efficient manifold embedding algorithm is proposed to transfer labels from training images into graphlets. It is further observed that there are numerous redundant graphlets that are not discriminative to semantic categories, which are abandoned by a graphlet selection scheme as they make no contribution to the subsequent segmentation. Thereafter, we use a Gaussian mixture model (GMM) to learn the distribution of the selected post-embedding graphlets (i.e., vectors output from the graphlet embedding). Finally, we propose an image segmentation algorithm, termed representative graphlet cut, which leverages the learned GMM prior to measure the structure homogeneity of a test image. Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets. Besides, our approach performs competitively to the fully-supervised segmentation models.
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/2288/92738]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Zhang, Luming,Gao, Yue,Xia, Yingjie,et al. Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation[J]. http://dx.doi.org/10.1109/TMM.2013.2293424,2014.
APA	Zhang, Luming.,Gao, Yue.,Xia, Yingjie.,Lu, Ke.,Shen, Jialie.,...&纪荣嵘.(2014).Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation.http://dx.doi.org/10.1109/TMM.2013.2293424.
MLA	Zhang, Luming,et al."Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation".http://dx.doi.org/10.1109/TMM.2013.2293424 (2014).