Personalized video summarization based on group scoring

Darabi, Kaveh; Ghinea, Gheorghiţă

doi:10.1109/chinasip.2014.6889254

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Their participation was not incentivised by any mean and they are fully informed in regards to ethical aspects of the experiments. Additionally, at the same time they are asked to annotate the video segments and to choose the representative key frame of each scene based on the method proposed in [3]. The assigned scores for each frame were then averaged to generate a singular value for that frame.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Personalized video summarization using sift

Darabi¹,

Ghinea²

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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In this paper, we propose a methodology for generating users' tailored video abstracts. First, video frames are scored by a group of video experts (operators) according to audio, visual and textual content of the video. Then, a matrix that contains the relevancy scores of each video scene into a number of pre-defined categories is computed using Scaled Invariant Feature Transform (SIFT) features, which are computed pairwise for representative keyframes and delegate images from the training collection. Later, for profiling purposes, an end-user's interest levels towards those high-level visual concepts (categories) are captured in the form of a vector. As a result of combining these two groups of data, the user's priorities in regards to different video segments can be determined. In the next stage, the initial averaged scores of the frames are updated based on the end-users' generated profiles. Eventually, the highest scored video frames alongside the auditory and textual content are inserted into final digest. The effectiveness of this approach has been evaluated by comparing the video summaries generated by this system against the results from a number of automatic and semi-automatic summarization tools that use different modalities for abstraction.

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Section: Experimental Evaluationmentioning

confidence: 99%

“…Afterwards, based on an approach explained in [2,3], the video experts (operators) are asked to score the video frames based on their personal interests and the perceived significance of the content they are watching. Figure 1 shows the scoring process of the video frames using a slider tool.…”

Section: Frames Saliency Detectionmentioning

confidence: 99%

Personalized video summarization using sift

Darabi¹,

Ghinea²

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

Self Cite

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“…The team also proposed developing new methods to better utilise incoherent audio information. Darabi and Ghinea [20] presented a new method for producing personalised video summaries. Experimental results indicate the effectiveness of this approach in delivering superior outcomes compared to previously proposed method with three other automatic summarisation tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality assessment framework for video contextualisation of personal videos

Sanap¹,

Apte²

2020

IET Image Processing

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“…User preferences, which are often involved in the expected results, should be taken into account in video summaries [9]. Therefore, it is important to modify the video summary to suit the user's interests and preferences, thus creating a personalized video summary, while retaining important semantic content from the original video [10]. Video summaries that reflect the understanding of individual users about the content of the video should be personalized in a way that is based on individual needs and intuitive.…”

Section: Introductionmentioning

confidence: 99%

“…Table 3 provides some datasets with their characteristics that were used for personalized video summarization. SumMe [80] 2014 Sport, event and holiday videos 25 [1,6] TVSum [81] 2015 Documentaries, news and egocentric videos 50 [2,10] FineGym [98] 2020 YouTube gymnasium videos 156 10…”

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confidence: 99%

Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets

Peronikolis,

Panagiotakis

2024

Applied Sciences

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In recent years, the scientific and technological developments have led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on the criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, keyframe selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

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Personalized video summarization based on group scoring

Cited by 6 publications

References 13 publications

Personalized video summarization using sift

Personalized video summarization using sift

Quality assessment framework for video contextualisation of personal videos

Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets

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