RETRACTED ARTICLE: A novel approach to automatic detection of presentation slides in educational videos

Zhao, Baoquan; Lin, Sheng; Qi, Xin; Wang, Ruomei; Luo, Xiangfeng

doi:10.1007/s00521-017-3276-1

Cited by 10 publications

(8 citation statements)

References 39 publications

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“…Wang et al [21,22] classified users into different clusters according to their drawing style and used them as prior information to facilitate image retrieval. There is also a large body of research that seeks to develop deep features for content-based image analysis and retrieval [23][24][25][26][27].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

Sheng

Wang

Zhao

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. As a consequence, the performance of existing SBIR systems is still far from being satisfactory. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

Sheng

Wang

Zhao

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…For example, the maximum peak of the color histogram and difference in entropy for horizontal lines were used to detect slide changes in [14]. Often the use of histograms for slide detection is supplemented with other algorithms to detect features such as faces, or text [20], [29].…”

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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“…with HCI now being central for supporting learners [1,8,11,16], blurring the boundary between search and recommendations and attempting to make learning more interactive, social and personalised [7]. Despite evidence highlighting the benefits of search and navigation tools in education [1,8,16], sophisticated features have yet to become mainstream in educational platforms. This intersection of AI and HCI in education brings to light fundamental research questions (both technical and pedagogical) including what is the nature of knowledge and how can it be understood and represented using AI at the user interface?…”

Section: Introductionmentioning

confidence: 99%

X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI

Pérez-Ortiz

Dormann

Rogers³

et al. 2021

26th International Conference on Intelligent User Interfaces

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X5Learn (available at https:// x5learn.org) is a human-centered AIpowered platform for supporting access to free online educational resources. X5Learn provides users with a number of educational tools for interacting with open educational videos, and a set of tools adapted to suit the pedagogical preferences of users. It is intended to support both teachers and students, alike. For teachers, it provides a powerful platform to reuse, revise, remix, and redistribute open courseware produced by others. These can be videos, pdfs, exercises and other online material. For students, it provides a scaffolded and informative interface to select content to watch, read, make notes and write reviews, as well as a powerful personalised recommendation system that can optimise learning paths and adjust to the user's learning preferences. What makes X5Learn stand out from other educational platforms, is how it combines human-centered design with AI algorithms and software tools with the goal of making it intuitive and easy to use, as well as making the AI transparent to the user. We present the core search tool of X5Learn, intended to support exploring open educational materials.

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RETRACTED ARTICLE: A novel approach to automatic detection of presentation slides in educational videos

Cited by 10 publications

References 39 publications

Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

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

X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI

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