Survey of Content Based Lecture Video Retrieval
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2015 by IJCTT Journal | ||
| Volume-19 Number-1 |
||
| Year of Publication : 2015 | ||
| Authors : Dipali Patil , Mrs. M. A. Potey | ||
| DOI : 10.14445/22312803/IJCTT-V19P102 |
Dipali Patil , Mrs. M. A. Potey "Survey of Content Based Lecture Video Retrieval". International Journal of Computer Trends and Technology (IJCTT) V19(1):5-8, Jan 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
In the last few years, e-lecturing has become more and more popular because video provide rich source of information. The amount of lecture video data on the internet is growing exponentially. Thus, a more ef?cient method for video retrieval in internet or within large lecture video archives is urgently needed. This paper presents a text based video retrieval and Video search system using Optimal Character Recognition (OCR). First, we convert the video into key-frames and extract the Text using OCR. Following step is to produce a summary presenting key points of the video, by making use of meradata of text and audio extracted from the Video. This summary will then be used for grouping and Indexing of videos. In this paper, we discuss various lecture video segmentation approaches. As there is strong needs for segmenting lecture videos into topic units in order to organize the videos for browsing and to provide search capability.
References
[1] Glass, James R., et al. "Recent progress in the MIT spoken lecture processing project." INTERSPEECH. 2007.
[2] NPTEL http://www.nptel.iitm.ac.in/
[3] Smoliar, Stephen W., and HongJiang Zhang. "Content-based video indexing and retrieval." IEEE multimedia 1.2 (1994): 62-72.
[4] freevideolectures.com http://www.freevideolectures.com/
[5] MITOPENCOURSEWARE http://ocw.mit.edu/courses/audio-video-courses/
[6] Smeaton, Alan F. "Techniques used and open challenges to the analysis, indexing and retrieval of digital video." Information Systems 32.4 (2007): 545-559.
[7] Tuna, Tailfin, et al. "Development and evaluation of indexed captioned searchable videos for STEM coursework." Proceedings of the 43rd ACM technical symposium on Computer Science Education. ACM, 2012.
[8] Jeong, Hyun Ji, Tak-Eun Kim, and Myoung Ho Kim. "An accurate lecture video segmentation method by using sift and adaptive threshold." Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia. ACM, 2012.
[9] Sack, Harald, and Jörg Waitelonis. "Integrating social tagging and document annotation for content-based search in multimedia data." Semantic Authoring and Annotation Workshop (SAAW). 2006.
[10] Moritz, F., M. Siebert, and C. Meinel. "Community tagging in tele-teaching environments." 2nd International Conference on e-Education, e-Business, e-Management and E-Learning (to appear). 2011.
[11] Leeuwis, Erwin, Marcello Federico, and Mauro Cettolo. "Language modelling and transcription of the TED corpus lectures." Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP`03). 2003 IEEE International Conference on. Vol. 1. IEEE, 2003.
[12] Munteanu, Cosmin, et al. "Automatic speech recognition for webcasts: how good is good enough and what to do when it isn`t." Proceedings of the 8th international conference on Multimodal interfaces. ACM, 2006.
[13] Adcock, John, et al. "Talk miner: a lecture webcast search engine. “Proceedings of the international conference on Multimedia. ACM, 2010.
[14] Nandzik, Jan, et al. "CONTENTUS—technologies for next generation multimedia libraries." Multimedia tools and applications 63.2 (2013): 287-329.
[15] Che, Xiaoyin, Haojin Yang, and Christoph Meinel. "Lecture video segmentation by automatically analysing the synchronized slides." Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013.
[16] Yamamoto, Natsuo, Jun Ogata, and Yasuo Ariki. "Topic segmentation and retrieval system for lecture videos based on spontaneous speech recognition."INTERSPEECH. 2003.
[17] Lin, Ming, et al. "Segmentation of lecture videos based on text: a method combining multiple linguistic features." System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on. IEEE, 2004.
[18] Epshtein, Boris, Eyal Ofek, and Yonatan Wexler. "Detecting text in natural scenes with stroke width transform." Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010.
[19] Yang, H., and C. Meinel. "Content Based Lecture Video Retrieval Using Speech and Video Text Information." (2014): 1-1.
[20] Jeong, Hyun Ji, Tak-Eun Kim, and Myoung Ho Kim. "An accurate lecture video segmentation method by using sift and adaptive threshold." Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia. ACM, 2012.
[21] Zhan, Yaowen, Weiqiang Wang, and Wen Gao. "A robust split-and-merge text segmentation approach for images." Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. Vol. 2. IEEE, 2006.
[22] Yang, Haojin, Bernhard Quehl, and Harald Sack. "A framework for improved video text detection and recognition." Multimedia Tools and Applications 69.1 (2014): 217-245.
Keywords
Content based Video Retrieval, Lecture Video Segmentation and Optical Character Recognition (OCR)