Encoded Semantic Tree for Automatic User Profiling Applied to Personalized Video Summarization

Yin, Yifang; Thapliya, Roshan; Zimmermann, Roger

doi:10.1109/tcsvt.2016.2602832

Cited by 15 publications

(22 citation statements)

References 40 publications

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“…However, a single image feature can not being able to fully characterize the frame content and complexity. In an effort to improve this problem, researchers provide some variants to compute the representative of the frames by extracting multiple features including entropy, motion information or region of interest [13,14,15,16,17].…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction and Clustering for Static Video Summarization

Sun

Liu

Jiang

2021

Preprint

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Numerous limitations of shot based and content based key frame extraction approaches have encouraged the development of cluster based methods. This work provides OTMW, Optimal Threshold and Maximum Weight clustering method, as a novel cluster based key frame extraction method. The video feature dataset is constructed by computing the color, texture and information complexity features of frame images. An optimization function is developed to compute the optimal clustering threshold. It is constrained by fidelity and ratio measure parameters. We turn to an empirical study on the proposed method in multi-type video key frame extraction tasks and compare it with popular cluster based methods including Mean-shift, DBSCAN, GMM and K-means. OTWM method achieves an average fidelity and ratio of 96.12 and 97.13, respectively. Experimental results demonstrate that OTMW can bring higher fidelity and ratio performance, while still maintaining a competitive performance over other cluster based methods. Overall, the proposed method can accurately extract key frames from multi-type videos.

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Section: Related Workmentioning

confidence: 99%

Feature Extraction and Clustering for Static Video Summarization

Sun

Liu

Jiang

2021

Preprint

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“…There are also some improved algorithms for extracting key frames based on semantic clustering. For example, Yin et al [ 13 ] constructed an innovative algorithm based on semantic connections among the elements. In order to construct SeTree, a normalized graph cut clustering algorithm by combining visual features, textual information and user preferences is proposed.…”

Section: Related Workmentioning

confidence: 99%

Video Summarization Based on Mutual Information and Entropy Sliding Window Method

Zhang

et al. 2020

Entropy

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This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and Fmeasure are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video.

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“…They also tried to solve the problem of curriculum generation using chromosome problems in graph theory, and the research work was deepened continuously [2]. Chen et al (2014) proposed the evolutionary design based on the structural gene model of life cycle assessment and established the genetic model of structural life cycle assessment characteristics [3]. They also converted the variable length coding to equal length coding to achieve quantitative representation of structural information.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Generation and Evolution of Personalized Curriculum Based on Genetic Algorithm

Duan¹

2019

Int. J. Emerg. Technol. Learn.

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In order to solve the automatic generation and evolution of personalized curriculum, the method of using genetic algorithm to realize the evolution of personalized learning content is proposed to solve the dynamic personalized needs of users. Through the research and implementation of personalized curriculum generation technology, firstly, the structures of curriculum generation, genetic algorithm and curriculum scene, as well as the method and technology of personalized curriculum generation and evolution system framework are described in detail. Then, the framework structure based on genetic algorithm is determined, and the user model is updated. Finally, experiments are carried out based on genetic algorithm. The research on the experiment of automatic generation and evolution of personalized curriculum shows that the application of genetic algorithm in the process of curriculum generation and evolution makes students' learning content evolve with the change of their knowledge state in the process of learning, effectively promotes students' interest in learning, and improves learning efficiency and effect.

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Encoded Semantic Tree for Automatic User Profiling Applied to Personalized Video Summarization

Cited by 15 publications

References 40 publications

Feature Extraction and Clustering for Static Video Summarization

Feature Extraction and Clustering for Static Video Summarization

Video Summarization Based on Mutual Information and Entropy Sliding Window Method

Automatic Generation and Evolution of Personalized Curriculum Based on Genetic Algorithm

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