互联网社群图像标签排序研究进展

摘要/Abstract

摘要： 互联网社群图像标签排序是目前计算机视觉、机器学习等领域最热门的课题之一。图像标签序列的合理性直接影响到图像检索等应用的效果。目前图像标签排序的方法多种多样,根据标签排序方法的不同将其划分为基于语义相关度与基于视觉显著性的标签排序,着重介绍了两类方法的典型标签排序方法,分析其各自的优缺点。最后就图像标签排序的评价方法以及发展趋势做了简单的论述。

关键词: 标签排序,相关性,显著性

Abstract: Tag ranking for internet social images is one of the most popular topics in the computer vision and machine learning.The effect of image retrieval and other applications is directly affected by the reasonableness of the order of image tag.Currently,the existing methods on tag ranking are various.This paper divided them into relevance-based tag ranking and saliency-based tag ranking.This paper highlighted two typical image tag ranking ways and analyzes their advantages and disadvantages respectively.Finally,we discussed the evaluation methods and trends of image tag ranking simply.

Key words: Tag ranking,Relevance,Saliency

吴焰樟,刘宏哲,冯松鹤,袁家政,张静怡. 互联网社群图像标签排序研究进展[J]. 计算机科学, 2015, 42(8): 22-27. https://doi.org/

WU Yan-zhang, LIU Hong-zhe, FENG Song-he, YUAN Jia-zheng and ZHANG Jing-yi. Advances in Tag Ranking for Internet Social Images[J]. Computer Science, 2015, 42(8): 22-27. https://doi.org/

参考文献

[1] Tang J,Yan S,Hong R,et al.Inferring semantic concepts from community-contributed images and noisy tags[C]∥Proceedings of the 17th ACM international conference on Multimedia.ACM,2009:223-232
[2] Xiang Y,Zhou X,Chua T S,et al.A revisit of generative model for automatic image annotation using markov random fields[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2009).IEEE,2009:1153-1160
[3] Zhang S,Tian Q,Hua G,et al.Descriptive visual words and vi-sual phrases for image applications[C]∥Proceedings of the 17th ACM international conference on Multimedia.ACM,2009:75-84
[4] Xiang Y,Zhou X,Liu Z,et al.Semantic context modeling with maximal margin conditional random fields for automatic image annotation[C]∥2010 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2010:3368-3375
[5] Ames M,Naaman M.Why we tag:motivations for annotation in mobile and online media[C]∥Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.ACM,2007:971-980
[6] Kennedy L S,Chang S F,Kozintsev I V.To search or to label ?:predicting the performance of search-based automatic image classifiers[C]∥Proceedings of 8th ACM International Workshop on Multimedia Information Retrieval.ACM,2006:249-258
[7] Liu D,Hua X S,Yang L,et al.Tag ranking [C]∥Proceedings of 18th International Conference on World Wide Web.ACM,2009:351-360
[8] Li X,Snoek C G M,Worring M.Learning tag relevance byneighbor voting for social image retrieval[C]∥Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval.ACM,2008:180-187
[9] Feng S,Xu D,Yang X.Attention-driven salient edge(s) and region(s) extraction with application to CBIR[J].Signal Proces-sing,2010,90(1):1-15
[10] Guillaumin M,Verbeek J,Schmid C.Multimodal semi-super-vised learning for image classification[C]∥2010 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2010:902-909
[11] Wang W,Lang C,Feng S.Contextualizing Tag Ranking and Saliency Detection for Social Images[M]∥Advances in Multimedia Modeling.Springer Berlin Heidelberg,2013:428-435
[12] Liu D,Hua X S,Zhang H J.Content-based tag processing for internet social images[J].Multimedia Tools and Applications,2011,51(2):723-738
[13] 刘东.基于内容的互联网社交图像标签处理与分析[D].哈尔滨:哈尔滨工业大学,2012 Liu D.Content-based tag processing and analysis for internet social images [D].Harbin:Harbin Institute of Technology,2012
[14] Zhao Y,Zha Z J,Li S,et al.Which tags are related to visual content?[M]∥Advances in Multimedia Modeling.Springer Berlin Heidelberg,2010:669-675
[15] Sun F,Li H,Zhao Y,et al.Towards tags ranking for social images[J].Neurocomputing,2013,120:434-440
[16] Li X,Snoek C G M,Worring M.Learning social tag relevance by neighbor voting[J].IEEE Transactions on Multimedia,2009,11(7):1310-1322
[17] Li X,Snoek C G M,Worring M.Unsupervised multi-feature tag relevance learning for social image retrieval[C]∥Proceedings of the ACM International Conference on Image and Video Retrie-val.ACM,2010:10-17
[18] Zhuang J,Hoi S C H.A two-view learning approach for image tag ranking[C]∥Proceedings of fourth ACM International Conference on Web Search and Data Mining.ACM,2011:625-634
[19] Li M,Tang J,Li H,et al.Tag ranking by propagating relevance over tag and image graphs[C]∥Proceedings of 4th International Conference on Internet Multimedia Computing and Service.ACM,2012:153-156
[20] 李曼先,赵春霞,唐金辉,等.基于视觉显著性近邻投票的标签排序方法[J].南京理工大学学报(自然科学版),2012,6(4):561-566 Li M X,Zhao C X,Tang J H,et al.Visual saliency neighbor voting based tag ranking[J].Journal of Nanjing University of Science and Technology(Natural Science),2012,36(4):561-566
[21] Tang J,Li M,Li Z,et al.Tag ranking based on salient region graph propagation[J].Multimedia Systems,2014:1-9
[22] Feng S,Lang C,Xu D.Beyond tag relevance:integrating visual attention model and multi-instance learning for tag saliency ranking[C]∥Proceedings of ACM International Conference on Image and Video Retrieval.ACM,2010:288-295
[23] Feng S,Lang C,Li B.Towards relevance and saliency ranking of image tags[C]∥Proceedings of the 20th ACM international conference on Multimedia.ACM,2012:917-920
[24] Feng S,Lang C,Liu H,et al.Adaptive all-season image tagranking by saliency-driven image pre-classification[J].Journal of Visual Communication and Image Representation,2013,24(7):1031-1039
[25] Barnard K,Duygulu P,Forsyth D,et al.Matching words and pictures[J].The Journal of Machine Learning Research 3,2003:1107-1135
[26] Jeon J,Lavrenko V,Manmatha R.Automatic image annotation and retrieval using crossmedia relevance models[Z].New York:ACM Press,2003
[27] Lavrenko V,Manmatha R,Jeon J.A Model for Learning the Semantics of Pictures[C]∥NIPS.2003,1:2
[28] Feng S L,Manmatha R,Lavrenko V.Multiple bernoulli rele-vance models for image and video annotation[C]∥ Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004
[29] Monay F,Gatica-Perez D.Modeling semantic aspects for cross-media image indexing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(10):1802-1817
[30] Itti L,Koch C,Niebur E.A model of saliency-based visual attention for rapid scene analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259
[31] Frintrop S,Klodt M,Rome E.A real-time visual attention system using integral images[C]∥Proceedings of 5th International Conference on Computer Vision Systems.2007
[32] 冯松鹤.面向感知的图像检索及自动标注算法研究[D].北京:北京交通大学,2009 Feng S H.Research on perception oriented image retrieval and automatic image annotation[D].Beijing:Beijing Jiaotong University,2009
[33] Cheng M M,Zhang G X,Mitra N J,et al.Global contrast based salient region detection[C]∥2011 IEEE Conference on ComputerVision and Pattern Recognition(CVPR).IEEE,2011:409-416
[34] Zha Z J,Wang M,Zheng Y T,et al.Interactive video indexing with statistical active learning[J].IEEE Transactions on Multimedia,2012,14(1):17-27
[35] Zha Z J,Yang L,Mei T,et al.Visual query suggestion[C]∥Proceedings of 17th ACM International Conference on Multimedia.ACM,2009:15-24
[36] Zha Z J,Hua X S,Mei T,et al.Joint multi-label multi-instance learning for image classification[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2008(CVPR 2008).IEEE,2008:1-8
[37] Yang Y,Yang Y,Shen H T.Effective transfer tagging fromimage to video[J].ACM Transactions on Multimedia Computing,Communications,and Applications(TOMCCAP),2013,9(2):14
[38] Chen Y,Bi J,Wang J Z.MILES:Multiple-instance learning via embedded instance selection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):1931-1947
[39] Yakhnenko O,Honavar V.Multi-Instance Multi-Label Learning for Image Classification with Large Vocabularies[C]∥BMVC.2011:1-12
[40] Jrvelin K,Keklinen J.Cumulated gain-based evaluation of IR techniques[J].ACM Transactions on Information Systems(TOIS),2002,20(4):422-446
[41] Baeza-Yates R,Ribeiro-Neto B.Modern information retrieval[M].New York:ACM press,1999
[42] Lan T,Mori G.A Max-Margin Riffled Independence Model for Image Tag Ranking[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2013:3103-3110

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