Personalized video summarization by highest quality frames

Darabi, Kaveh; Ghinea, Gheorghiţă

doi:10.1109/icmew.2014.6890674

Cited by 6 publications

(9 citation statements)

References 16 publications

(16 reference statements)

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“…In this phase, the primary generated average scores of the frames, assigned by the video scorers and averaged over number of operators are updated based on the approach introduced in [4]. Thus, based on computed priority level for each video segment for a particular user, the primary average scores are updated.…”

Section: Updating the Initial Frame Scoresmentioning

confidence: 99%

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: Updating the Initial Frame Scoresmentioning

confidence: 99%

Personalized video summarization using sift

Darabi¹,

Ghinea²

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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“…Darabi and Ghinea [34] proposed a framework to produce personalized video summaries based on ratings assigned by video experts in video frames, and their second method could demonstrate good results for the movie category. Lie and Hsu [14] to evaluate their proposed personalized video summarization system, they collected movies to conduct experiments.…”

Section: Personalized Trailer Of Automated Serial Movies Documentarie...mentioning

confidence: 99%

“…• Static story boards are also called static summaries. Some personalized video summaries consist of a set of static images that extract highlights, and their representation is made as a photo album [15,16,[21][22][23]29,33,34,42,43,47,[51][52][53][54][55][55][56][57][58]. The generation of these summaries is done by extracting the keyframes according to the user's preferences and the summary criteria.…”

Section: Type Of Personalized Summarymentioning

confidence: 99%

“…For the immediate identification of the players in the moving frames, they used the Deep Learning method for the identification of the player, and for the modeling of the football field, the Gaussian mixture model (GMM) method to achieve background removal. AVCutty was used in the work of Darabi et al [34] to detect the boundaries of the scene and thus detect when a change in scene occurs through the motion and color features of the frames. The study by Varini et al [61] is also motion-based.…”

Section: Motion and Color Basedmentioning

confidence: 99%

“…The system proposed by Kannan et al [66] can generate a video summarization without relying on any specific domain. The summaries presented in [12][13][14]17,20,22,25,27,30,31,33,34,45,47,51,54,55,[57][58][59][60][63][64][65][67][68][69][70][71]73] are not domain specific. The types of domains found in the literature are Personal Videos [52,53], Movies / serial clips [19,28,29,35,49], Sports Videos [18,35,[37][38][39][40][41][42][43][44],…”

Section: Video Domainmentioning

confidence: 99%

See 2 more Smart Citations

Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets

Peronikolis,

Panagiotakis

2024

Preprint

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In recent years, the scientific and technological developments 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 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, key frame 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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User-centred personalised video abstraction approach adopting SIFT features

Darabi

Ghinea

2016

Multimed Tools Appl

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Personalized video summarization by highest quality frames

Cited by 6 publications

References 16 publications

Personalized video summarization using sift

Personalized video summarization using sift

Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets

User-centred personalised video abstraction approach adopting SIFT features

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