Automatic detection of slide transitions in lecture videos

Jeong, Hyun Ji; Kim, Tak-Eun; Kim, Hyeon Gyu

doi:10.1007/s11042-014-1990-6

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…However, the segmentation of educational videos poses a specific challenge since a topic transition does not necessarily evoke changes in shown content (e.g., in lectures) and vice versa. Attempts to solve this problem make use of speech transcripts and superimposed text ( Tuna et al, 2015 ) or detection of slide changes ( Jeong et al, 2015 ) to achieve a better performance.…”

Section: Componentsmentioning

confidence: 99%

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

et al. 2022

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Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.

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Section: Componentsmentioning

confidence: 99%

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

et al. 2022

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“…This approach detects slide transitions when the SIFT similarity is under a defined threshold. Features extracted using the SIFT algorithm have shown good slide detection accuracy rates in [10], [22] and with slide alignment [28]. SIFT features can also be used with sparse time-varying graphs [17], where the graph models slide transitions.…”

Section: A Slide Transition Detectionmentioning

confidence: 99%

SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks

Sindel¹,

Hernandez²,

Yang³

et al. 2022

Preprint

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With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming. Therefore, automatic slide extraction from the lecture videos can be helpful to give a brief overview of the main content and to support the students in their studies. For this task, we propose a deep learning method to detect slide transitions in lectures videos. We first process each frame of the video by a heuristic-based approach using a 2-D convolutional neural network to predict transition candidates. Then, we increase the complexity by employing two 3-D convolutional neural networks to refine the transition candidates. Evaluation results demonstrate the effectiveness of our method in finding slide transitions.

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“…Whereas, the frames appearance will change with the camera motion. Hyun et al [5] and Jeong et al [6] extracted SIFT features, which are invariant to image scaling and rotation, and match them between two adjacent frames. The slide transition is detected when the similarity based on SIFT similarity is smaller than a threshold.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Residual Networks for Slide Transition Detection in Lecture Videos

2019

KSII TIIS

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In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes. Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos. However, 3D ConvNet will cost much training time and need lots of memory. Hence, we utilize ResNet to ease the training of network, which is easy to optimize. Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches.

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Automatic detection of slide transitions in lecture videos

Cited by 11 publications

References 16 publications

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks

Spatio-Temporal Residual Networks for Slide Transition Detection in Lecture Videos

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