Extracting content from instructional videos by statistical modelling and classification

Choudary, Chekuri; Liu, Tiecheng

doi:10.1007/s10044-006-0051-9

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…The annotated videos support and drive the students to watch and learn from the videos by an E-Learning archived video. C. Choudary and T. Liu [6], offered a robust technique to appropriately extract the textual content and images from instructional videos. They investigated that the delivery of content material pixels in instructional videos developes a statistical version for classifying the content material pixels.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Annotation of Instructional Videos

Bhavitha*,

Kalaivani

2020

IJRTE

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Multimedia has a significant role in communicating the information and a large amount of multimedia repositories make the browsing, retrieval and delivery of video contents. For higher education, using video as a tool for learning and teaching through multimedia application is a considerable promise. To extract the audio information from the visual content is a very challenging because it needs the extraction of high level semantic information from low level visual data. The summarization technique for videos has been proposed to improve the browsing faster for large video collections to produce more efficient content indexing and access. The proposed video summarization system for instructional videos initially separates videos into audio track and then converted into text transcript. The text transcripts are preprocessed and extracted the important relevant keywords which can be used for indexing the video file. The automatic annotation of instructional videos reduced the summary length and also preserved the semantic of the videos. The research indicates that the students found these summarized content is very helpful for preparing and reviewing the lecture material and also for the preparation during their examination.

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Section: Literature Surveymentioning

confidence: 99%

Automatic Annotation of Instructional Videos

Bhavitha*,

Kalaivani

2020

IJRTE

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“…In this work we are not aiming at exhaustively extracting all blackboard contents to create a lecture "handout" (Choudary and Liu 2007), nor we intend to apply trainedbased handwritten recognition (Plötz et al 2008) but to extract only the most relevant blackboard features that can visually summarize a recorded lecture.…”

Section: Blackboard Edits As Summarization Cuesmentioning

confidence: 99%

Proceedings of the 16th World Conference on Mobile and Contextual Learning

Specht¹,

Sharples²,

Multisilta³

2017

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“…In this work we are not aiming at exhaustively extracting all blackboard contents to create a lecture "handout" (Choudary & Liu, 2007;Zhang & He, 2004), nor we intend to apply trained-based handwritten recognition (Plötz, Thurau, & Fink, 2008;Wienecke, Fink, & Sagerer, 2005) but to extract only the most relevant blackboard features that visually summarize a blackboard-based recorded lecture.…”

Section: Blackboard Edits As Summarization Cuesmentioning

confidence: 99%

Advanced Mobile Lecture Viewing

Rivera

Nishihara

2012

International Journal of Handheld Computing Research

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In this work, the authors present a fully automated recorded lecture summarization tool and an innovative mobile iPad visualization tool. Summarization works for blackboard-based lectures by robustly extracting blackboard edits with great accuracy and high performance. Analysis output is then presented with overview and visual timelines along the original video to allow discovering lecture passages based on time. A second option allows students navigating contents in space by allowing revisiting blackboard elements directly by touching regions in the video. Proposed summarization, temporal and spatial navigation along with interactive visual annotations bring to recorded lectures the benefits that other digital learning material has long enjoyed and entice a younger generation of learners.

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Extracting content from instructional videos by statistical modelling and classification

Cited by 10 publications

References 26 publications

Automatic Annotation of Instructional Videos

Automatic Annotation of Instructional Videos

Proceedings of the 16th World Conference on Mobile and Contextual Learning

Advanced Mobile Lecture Viewing

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