Advanced SearchSearch Tips
Ontology Modeling and Rule-based Reasoning for Automatic Classification of Personal Media
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of KIISE
  • Volume 43, Issue 3,  2016, pp.370-379
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.3.370
 Title & Authors
Ontology Modeling and Rule-based Reasoning for Automatic Classification of Personal Media
Park, Hyun-Kyu; So, Chi-Seung; Park, Young-Tack;
Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.
ontology modeling;description logic;sequence based rule;automated classification;
 Cited by
J.H. Choi, S.C. Kim, Y.T. Park, "Ontology Representation for Personal Media Management," Proc. of the KIISE Korea Computer Congress 2008, pp. 98-99, 2008. (in Korean)

LIPTON, Alan J.; FUJIYOSHI, Hironobu; PATIL, Raju S. Moving target classification and tracking from real-time video, In: Applications of Computer Vision, 1998, WACV'98. Proceedings, Fourth IEEE Workshop on. IEEE, pp. 8-14, 1998.

Krotzsch, Markus, Frantisek Simancik, and Ian Horrocks, "A description logic primer," arXiv preprint arXiv:1201.4089, 2012.

Sirin, Evren, et al., "Pellet: A practical owl-dl reasoner," Web Semantics: science, services and agents on the World Wide Web 5.2, pp. 51-53, 2007. crossref(new window)

LU, Lie; ZHANG, Hong-Jiang; JIANG, Hao. Content analysis for audio classification and segmentation. Speech and Audio Processing, IEEE Transactions on, 10.7: 504-516, 2002. crossref(new window)

YOON, Yong-Ik; CHUN, Jee-Ae, Tracking Model for Abnormal Behavior from Multiple Network CCTV Using the Kalman Filter. In: Computer Science and its Applications. Springer Berlin Heidelberg, pp. 933-939, 2015.

DAVIES, Anthony C.; VELASTIN, Sergio A. Progress in computational intelligence to support cctv surveillance systems, International Journal of Computing, 4.3: 76-84, 2014.

SZEGEDY, Christian; TOSHEV, Alexander; ERHAN, Dumitru. Deep neural networks for object detection, In: Advances in Neural Information Processing Systems, pp. 2553-2561, 2013.

LI, Dongge, et al. Classification of general audio data for content-based retrieval, Pattern recognition letters, 22.5: 533-544, 2001. crossref(new window)

MANDEL, Michael I.; ELLIS, Daniel PW. Songlevel features and support vector machines for music classification, In: ISMIR 2005: 6th International Conference on Music Information Retrieval: Proceedings: Variation 2: Queen Mary, University of London & Goldsmiths College, University of London, 11-15 September, 2005, Queen Mary, University of London, pp. 594-599, 2005.

S. Espinosa, A. Kaya, S. Melzer, R. Moller, and M. Wessel, Towards a media interpretation framework for the semantic web, In The 2007 IEEE/WIC/ACM International Conference on Web Intelligence (WI'07), Fremont, USA, pp. 374-380, 2007.