A novel user-centered design for personalized video summarization

Ghinea, Gheorghiţă; Kannan, Rajkumar; Swaminathan, Sridhar; Kannaiyan, Suresh

doi:10.1109/icmew.2014.6890642

Cited by 9 publications

(23 citation statements)

References 12 publications

(17 reference statements)

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“…Several concepts identified in the video subtitles and video frames could refer to the same concept (same lemma), but have different forms. Thus, to better align the concepts we first run part-of-speech (POS) tagging 7 on the concepts from the video subtitles and from the video frames and then we extract their lemmas 8 . This resulted in an average of 36 entities (minimum 0, maximum 58) per video and an average of 175 labels (minimum 34, maximum 524) per video.…”

Section: Explanation Generation Methodologymentioning

confidence: 99%

“…Video summary personalization focuses on generating video summaries given a user's query [24,25,28]. Ghinea et al [8] proposed a summarization algorithm that creates a user profile, i.e., the user sees a set of 25 concepts present in the video and indicates through a list or a sliding window which or how much of these concepts should be included in the summary. Jin et al [14] integrated fastforward functionality, allowing users to skip parts of the summaries, while Chongtay et al [3] introduced the idea of responsive news summarization, i.e., automatically creating news summaries of different lengths, while providing access to the full news item.…”

Section: Personalization Of Video Summariesmentioning

confidence: 99%

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Eliciting User Preferences for Personalized Explanations for Video Summaries

Inel

Tintarev

Aroyo

2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Video summaries or highlights are a compelling alternative for exploring and contextualizing unprecedented amounts of video material. However, the summarization process is commonly automatic, non-transparent and potentially biased towards particular aspects depicted in the original video. Therefore, our aim is to help users like archivists or collection managers to quickly understand which summaries are the most representative for an original video. In this paper, we present empirical results on the utility of different types of visual explanations to achieve transparency for end users on how representative video summaries are, with respect to the original video. We consider four types of video summary explanations, which use in different ways the concepts extracted from the original video subtitles and the video stream, and their prominence. The explanations are generated to meet target user preferences and express different dimensions of transparency: concept prominence, semantic coverage, distance and quantity of coverage.In two user studies we evaluate the utility of the visual explanations for achieving transparency for end users. Our results show that explanations representing all of the dimensions have the highest utility for transparency, and consequently, for understanding the representativeness of video summaries. CCS CONCEPTS• Human-centered computing → User studies; Empirical studies in HCI.

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Section: Explanation Generation Methodologymentioning

confidence: 99%

Section: Personalization Of Video Summariesmentioning

confidence: 99%

Eliciting User Preferences for Personalized Explanations for Video Summaries

Inel

Tintarev

Aroyo

2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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show abstract

“…High-level video features are used as preferences to the users for personalized video summarization [15][16][17][18][19][20]. IBM research has proposed a personalized video summarization system for pervasive mobile devices such as PDA [15].…”

Section: Personalized Video Summarizationmentioning

confidence: 99%

“…Same as their method the proposed summarization also uses constrained optimization for selecting shots and scenes that are relevant to individual us- 6 er's preferences. Our previous work in [19] proposed a personalized video summarization methodology for summarizing a video based on users' preferences on a set of semantic concepts.…”

Section: Personalized Video Summarizationmentioning

confidence: 99%

“…A generic video summarization system that produces video summaries based only on low-level video features cannot process a semantic level query from a user, such as 'summarize all the action events of a movie'. Personalized or tailored video summarization is a useful technique for producing tailored video summaries to the users based on their needs and interests [18,19]. Also, recent information retrieval and filtering systems have started tailoring or personalizing the results by adjusting to individual user's needs and interests.…”

Section: Introductionmentioning

confidence: 99%

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What do you wish to see? A summarization system for movies based on user preferences

Kannan

Ghinea

Swaminathan³

2015

Information Processing & Management

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Abstract. Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.

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User-centred personalised video abstraction approach adopting SIFT features

Darabi

Ghinea

2016

Multimed Tools Appl

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A novel user-centered design for personalized video summarization

Cited by 9 publications

References 12 publications

Eliciting User Preferences for Personalized Explanations for Video Summaries

Eliciting User Preferences for Personalized Explanations for Video Summaries

What do you wish to see? A summarization system for movies based on user preferences

User-centred personalised video abstraction approach adopting SIFT features

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