Abnormal Facebook Multimedia Detection on Facebook using IQR Method
DOI:
https://doi.org/10.26438/ijcse/v5i8.141146Keywords:
Outliers, Inter-Quartile Range, Facebook Multimedia Outliers, Facebook, MetadataAbstract
Recently, discovering outliers among large scale Facebook multimedia have attracted attention of many Facebook mining researchers. There are number of outlier multimedia exists in each category of Facebook multimedia such as- ‘Entertainment’, ‘Sports’, ‘News and Politics’, etc. The task of identifying and manipulate (to remove from the Facebook or to share with others in the Facebook, or to watch/download from the Facebook etc.) such outlier Facebook multimedia have gained significant important research aspect in the area of Facebook Mining Research. In this work, we propose a novel method to detect outliers from the Facebook multimedia based on their metadata objects. Large scale Facebook multimedia metadata objects such as- length, view counts, numbers of comments, rating information are considered for outliers’ detection process. The outlier detection method–Inter-Quartile Range (IQR) is used to find outlier Facebook multimedia of same age. The resultant outliers are analysed and compared as a step in the process of knowledge discovery.
References
Chueh-Wei Chang, Ti-Hua Yang and Yu-Yu Tsao, “Abnormal Spatial Event Detection and Multimedia Content Searching in a Multi-Camera Surveillance System”, MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN.
Fan Jiang, Ying Wu, Aggelos K. Katsaggelos, “Abnormal Event Detection from Surveillance Multimedia by Dynamic Hierarchical Clustering”, Northwestern University, USA.
Tushar Sandhan et al., “Unsupervised learning approach for abnormal event detection in surveillance multimedia by revealing infrequent patterns”, IEEE 28th International Conference on Image and Vision Computing, 2013- New Zealand
Thi-Lan Le and Thanh-Hai Tran, “Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization”, Advances in Intelligent Systems and Computing 341, DOI 10.1007/978-3-319-14633-1_2, Springer International Publishing-2015, Switzerland.
Yang Cong et al., “Abnormal Event Detection in Crowded Scenes using Sparse Representation”, Pattern Recognition, January 30, 2013
Cewu Lu et al., “Abnormal Event Detection at 150 FPS in MATLAB”, The Chinese University of Hong Kong.
Y. Cong, J. Yuan, and J. Liu, “Sparse reconstruction cost for abnormal event detection,” CVPR 2011, Jun. 2011.
Bin Zhao et al., “Online Detection of Unusual Events in Multimedia via Dynamic Sparse Coding”, 2011.
Mahmoudi Sidi Ahmed et al., “Detection of Abnormal Motions in Multimedia”, Chania ICMI-MIAUCE’08 workshop, Crete, Greece, 2008.
Du Tran et al., “Multimedia Event Detection: From Subvolume Localization to Spatiotemporal Path Search”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, 2014.
Dataset for "Statistics and Social Network of YouTube Multimedia", http://netsg.cs.sfu.ca/youtubedata/.
Siddu P. Algur, Prashant Bhat, “Metadata Based Classification and Analysis of Large Scale Facebook Multimedia”, International Journal of Emerging Trends and Technologies in Computer Science, May-June 2015.
Siddu P. Algur, Prashant Bhat, "Abnormal Web Video Prediction Using RT and J48 Classification Techniques", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.101-107, 2016.
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