基于社会网络的图像语义获取研究综述

计算机与现代化 ›› 2014, Vol. 0 ›› Issue (1): 126-131,136.

基于社会网络的图像语义获取研究综述

1．金陵科技学院信息技术学院，江苏南京211169；2.北京京师普教文化传媒有限公司，北京100082；
3．河海大学计算机与信息学院，江苏南京210098

收稿日期:2013-07-26 出版日期:2014-01-20 发布日期:2014-02-10
作者简介:郭海凤（1980-），女，山东枣庄人，金陵科技学院信息技术学院讲师，河海大学计算机与信息学院博士研究生，研究方向：信息检索，模式识别；李广水（1965-），男，江苏扬州人，教授，博士，研究方向：图像理解，机器学习，基于内容的视觉信息检索；韩立新（1967-），男，江苏六合人，教授，博士生导师，研究方向：信息检索，模式识别。
基金资助:
江苏省大学生实践创新重点立项项目（201313573011Z）

Semantic Extracting Research Progress Based on Internet Social Images

1. Institute of Information Technology, Jinling Institute of Technology, Nanjing 211169, China;

2. Jingshi General Education Media Co.Ltd. of Beijing, Beijing 100082, China;
3. College of Computer and Information Engineering, Hohai University, Nanjing 210098, China

Received:2013-07-26 Online:2014-01-20 Published:2014-02-10

摘要/Abstract

摘要： 对Web 2.0用户标注系统中图像语义获取进展进行综述，分析社会图像语义获取的主要研究领域，包括标签去噪、自动标注、标签排序以及社会图像的2个典型应用：推荐系统和图像检索。在具体归纳过程中，阐述某技术或算法的主要依据是其被采用的广泛程度以及在大规模网络环境下的可行性，并力争保持内容组织的内在关联及层次特点。通过分析相关研究结果的统计数据及社会网的发展趋势，给出进一步研究思路。

关键词: 社会图像, 语义获取, 图像标注, Flickr网站

Abstract: This paper provides a comprehensive survey of the semantics extraction from images that is annotated by user in the Web 2.0 system. The main fields of semantics extracted from the social images are generalized, including tags denoising, automatic annotation, tag ranking and two application: recommendation system and image retrieval. The rule of selecting source technique mainly bases on its content it is paid much more attention by others and suitability for large-scale network environment, it’s also done to array the content organization relation and hierarchy characteristic. After the analysis of statistics results from the relevant researchs and social network development trend, summary and next research thought are presented.

Key words: social images, semantic extracting, image annotation, Flickr website

郭海凤1，3，张盈盈2，李广水1，韩立新3. 基于社会网络的图像语义获取研究综述[J]. 计算机与现代化, 2014, 0(1): 126-131,136.

GUO Hai-feng1,3, ZHANG Ying-ying2, LI Guang-shui1, HAN Li-xin3. Semantic Extracting Research Progress Based on Internet Social Images[J]. Computer and Modernization, 2014, 0(1): 126-131,136.

参考文献

［1］
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 the 14th ACM International Workshop on Multimedia Information Retrieval. 2006:249-258.
［2］ Sigurbjrnsson B, Zwol van R. Flickr tag recommendation based on collective knowledge［C］// Proceedings of the 17th International Conference on World Wide Web. 2008:327-336.
［3］ Liu Dong, Hua Xian-Sheng, Zhang Hong-Jiang. Content-based tag processing for Internet social images［J］. Multimedia Tools and Applications, 2011,51(2):723-738.
［4］ Hu J, Wang B, Liu Y, et al. Personalized tag recommendation using social in using social influence ［J］. Journal of Computer Science and Technology, 2012,27(3):527-540.
［5］ Liu B. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data［M］. Springer, 2006.
［6］ Leydesdorff L， Vaughan L． Co-occurrence matrices and their applications in information science: Extending ACA to the Web environment［J］． Journal of the American Society for Information Science and Technology, 2006,57(12):1616-1628． 
［7］ Liu Dong, Hua Xian-Sheng, Yang Linjun， et al. Tag ranking［C］// Proceedings of the 18th International Conference on World Wide Web. 2009:351-360.
［8］ Lu Y, Zhang L, Tian Q, et al. What are the high-level concepts with small semantic gaps?［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2008:1-8. 
［9］李晓燕,陈刚,寿黎但,等. 一种面向协作标签系统的图片检索聚类方法［J］. 中国图象图形学报, 2010,15(11):1635-1643.
［10］Yang Yang, Huang Zi, Shen Heng Tao, et al. Mining multi-tag association for image tagging［J］. World Wide Web, 2011,14(2):133-156.
［11］Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation［J］. Journal of Machine Learning Research, 2003（3）:993-1022.
［12］陈烨,邵健,朱科. 基于社群隐含主题挖掘和多社群信息融合的自动图像标注［J］. 中国图象图形学报, 2010,15(6):944-950.
［13］Wang Jingdong, Zhao Zhe, Zhou Jiazhen, et al. Recommending Flickr groups with social topic model［J］. Information Retrieval for Social Media, 2012,15(3-4):278-295.
［14］Liu J S. Monte Carlo Strategies in Scientific Computing［M］. Berlin: Springer, 2002.
［15］Wang Z, Feng H, Zhang C, et al. Learning to rank tags［C］// Proceedings of the ACM International Conference on Image and Video Retrieval. 2010：42-49.
［16］赵英海,查正军,李珊珊,等. 基于视觉内容语义相关度的图像标签自动排序方法［J］. 中国科技大学学报, 2011,41(2):108-115.
［17］Li X, Snoek C, Worring M. Learning tag relevance by neighbor voting for social image retrieval［C］// Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval. 2008:180-187.
［18］Li X, Snoek C, Worring M. Unsupervised multi-feature tag relevance learning for social image retrieval［C］// Proceedings of the ACM International Conference on Image and Video Retrieval, 2010:10-17.
［19］Kennedy L, Slaney M, Weinberger K. Reliable tags using image similarity: Mining specificity and expertise from large-scale multimedia databases［C］// Proceedings of the 1st Workshop on Web-scale Multimedia Corpus. 2009:17-24. 
［20］Chen Xian, Shin Hyoseop. Tag recommendation by machine learning with textual and social features［J］. Journal of Intelligent Information Systems, 2013,40(2):261-282.
［21］Negoescu R A, Gatica-Perez D. Modeling flickr communities through probabilistic topic-based analysis［J］. IEEE Transactions on Multimedia, 2010,12(5):399-416.
［22］Zheng N, Li Q, Liao S, et al. Which photo groups should I choose? A comparative study of recommendation algorithms in flickr［J］. Journal of Information Science, 2010,36(6): 733-750.
［23］Yu J, Joshi D, Luo J. Connecting people in photo-sharing sites by photo content and user annotations［C］// Proceedings of the 2009 IEEE International Conference on Multimedia and Expo. 2009:1464-1467.
［24］Yu J, Jin X, Han J, et al. Mining personal image collection for social group suggestion［C］// Proceedings of the 2009 IEEE International Conference on Data Mining Workshops. 2009:202-207.
［25］Jeong Jin-Woo, Hong Hyun-Ki, Lee Dong-Ho. i-TagRanker: An efficient tag ranking system for image sharing and retrieval using the semantic relationships between tags［J］. Multimedia Tools and Applications, 2013,62(2):451-478. 
［26］Lee Sangjin, Park Jonghun. Topic based photo set retrieval using user annotated tags［J］. Multimedia Tools and Applications, 2013,64(1):7-26.
［27］Sivic J, Zisserman A. Video Google: A text retrieval approach to object matching in videos［C］// Proceedings of the Ninth IEEE International Conference on Computer Vision. 2003:1470-1477.
［28］Li F F, Perona P. A Bayesian hierarchical model for learning natural scene categories［C］// Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005:524-531.
［29］Nistér D, Stewénius H. Scalable recognition with a vocabulary tree［C］// Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006:2161-2168.
［30］ Fraundorfer F, Stewénius H, Nistér D. A binning scheme for fast hard drive based image search［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2007：1-6. 
［31］Jégou H, Harzallah H, Schmid C. A contextual dissimilarity measure for accurate and efficient image search［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2007:1-8.
［32］Philbin J, Chum O, Isard M, et al. Lost in quantization: Improving particular object retrieval in large scale image databases［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2008:1-8.
［33］Hervé Jégou, Matthijs Douze, Cordelia Schmid. Improving Bag-of-Features for large scale image search［J］. International Journal of Computer Vision, 2010,87(3):316-336.
［34］庄凌,庄越挺,吴江琴,等. 基于稀疏典型性相关分析的图像检索方法［J］. 软件学报, 2012,23(5):1295-1304.
［35］ 许红涛,周向东,向宇,等. 一种自适应的 Web 图像语义自动标注方法［J］. 软件学报, 2010,21(9):2183-2195.