Key Frames Extraction Using Spline Curve Fitting for Online Video Summarization

Ghani, Rana Fareed; Mahmood, Sawsen Abdulhadi; Jurn, Yaseen Naser; Al-Jobouri, Laith

doi:10.1109/ceec47804.2019.8974340

Cited by 5 publications

(2 citation statements)

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“…The user may not be interested in the complete video's content, or the user may not have enough time to watch the entire video. People may only want to see the abstraction of the video rather than the entire video [1], [2]. Abstraction is a process that delivers a fast overview of the most important or relevant information within the original content.…”

Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

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<p><span lang="EN-US">Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.</span></p>

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Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

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“…Summarization is the process which provides a short information over those whole media, that demonstrates fast thought of the greater part imperative or important majority of the data inside the original content in little amount of time thus it is save client time, lessen storage, furthermore increase transfer speed for transmission through that web. However, the summarization of video, audio, picture, and text is complex task because of its sequence-to-sequence nature [1]- [3]. In this way, this paper takes text from lecture file then performs text summarization.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Recurrent Neural Networks for Text Lecture Summarization

2022

IJCCCE

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Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy. Index Terms— Text Summarization, Deep Learning, Convolutional Neural Network, Long Short Term Memory, Convolutional Recurrent Neural Network.

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Shot Boundaries Detection Based Video Summary Using Dynamic Time Warping and Mean Shift

Sulaiman

Mahmood

2020

2020 International Conference on Computer Science and Software Engineering (CSASE)

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Key Frames Extraction Using Spline Curve Fitting for Online Video Summarization

Cited by 5 publications

References 5 publications

Multimodal video abstraction into a static document using deep learning

Multimodal video abstraction into a static document using deep learning

Convolutional Recurrent Neural Networks for Text Lecture Summarization

Shot Boundaries Detection Based Video Summary Using Dynamic Time Warping and Mean Shift

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