A Novel Approach to Mine Knowledge from Social Images

Abstract:

With the popularity of various social media website, currently, lots of social images attached with different kinds of metadata have been uploaded to social media websites. Mining useful knowledge from social images has been an emerging important research topic in web search and data mining. In this paper, we propose a novel approach to find geographical difference of a given concept from social image community. We put a given concept to social image community, and then downloaded social images with metadata, particularly, the place where the photo was taken should be provided in advance. Firstly, concept is submitted to social image community, and then social images with different kinds of metadata are downloaded. Secondly, social images are clustered according to metadata of images. Finally, the information of concept’s geographical difference is found. Experiments conducted on social image community proof the effectiveness of our approach. Keywords: Social Images, Data Mining, Social Image Community, Image Clustering.

• Periodical: Advanced Materials Research (Volumes 430-432)
• Pages: 1068-1071
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.430-432.1068
• Online since: January 2012
• Authors: Fei Chao Wang
• Keywords: Data Mining (DM), Image Clustering, Social Image Community, Social Images

References

[1] Pengcheng Wu, Steven C.H. Hoi, Peilin Zhao, Ying He. Mining Social Images with Distance Metric Learning for Automated Image Tagging. WSDM (2011).

DOI: 10.1145/1935826.1935865

[2] Yue Gao; Jinhui Tang, Richang Hong, Qionghai Dai, Tat-Seng Chua, Ramesh Jain. W2Go: A Travel Guidance System by Automatic Landmark Ranking. MM (2010).

DOI: 10.1145/1873951.1873970

[3] R. Abbasi, S. Chernov, W. Nejd, R. Paiu, and S. Staab. Exploiting flickr tags and groups for finding landmark photos. ECIR (2009).

DOI: 10.1007/978-3-642-00958-7_62

[4] D.J. Crandall, L. Backstrom, D. et al. Mapping the world's photos. WWW (2009).

[5] Lyndon Kennedy and M. Naaman. Generating diverse and representative image search results for landmarks. WWW (2008).

DOI: 10.1145/1367497.1367539

[6] K. Yanai, H. Kawakubo, and B. Qiu. A visual analysis of the relationship between word concepts and geographical locations. CIVR (2009).

DOI: 10.1145/1646396.1646414

[7] Y. Zheng, M. Zhao, et al. Tour the world: building a web-scale landmark recogntion engine. CVPR (2009).

[8] Adrian Popescu, Pierre-Alain Moëllic. MonuAnno: Automatic Annotation of Georeferenced Landmarks Images, CIVR (2009).

DOI: 10.1145/1646396.1646412

[9] H. Hile, R. Vedantham, A. Liu, N et al., Landmark-Based Pedestrian Navigation from Collections of Geotagged Photos, ACM MUM, (2008).

DOI: 10.1145/1543137.1543167

[10] W. Chen, A. Battestini, N. Gelfand, and V. Setlur. Visual summaries of popular landmarks from community photo collections. In ACM international conference on Multimedia, pp.789-792, (2009).

DOI: 10.1145/1631272.1631415

[11] Qiu, G. Image and Feature Co-clustering. ICPR 2004, pp.991-994, (2004).

[12] Bin Gao, Tie-Yan Liu, Tao Qin et al. Web image clustering by consistent utilization of visual features and surrounding texts. MM2005, 2005, pp.112-121.

[13] Dhillon, I.S. Co-clustering documents and words using bipartite spectral graph partitioning. KDD'01, (2001).

DOI: 10.1145/502512.502550