Toward a conceptual framework of key‐frame extraction and storyboard display for video summarization

Kim, Hyun Hee; Kim, Yong Ho

doi:10.1002/asi.21317

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…In a study of key‐frame extraction, Kim and Kim () reviewed six representative models for still image analysis, concluding that people interact with images at three levels: primitive features (e.g., color and shape); derived attributes (e.g., specific objects), and semantic abstract attributes (e.g., the symbolic value) (Greisdorf & O'Connor, ), which resemble the three Panofskian levels.…”

Section: Related Workmentioning

confidence: 99%

Time‐based tags for fiction movies: comparing experts to novices using a video labeling game

Estrada

Hildebrand

Boer

et al. 2016

Asso for Info Science & Tech

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The cultural heritage sector has embraced social tagging as a way to increase both access to online content and to engage users with their digital collections. In this article, we build on two current lines of research. (a) We use Waisda?, an existing labeling game, to add timebased annotations to content. (b) In this context, we investigate the role of experts in human-based computation (nichesourcing). We report on a small-scale experiment in which we applied Waisda? to content from film archives. We study the differences in the type of timebased tags between experts and novices for film clips in a crowdsourcing setting. The findings show high similarity in the number and type of tags (mostly factual). In the less frequent tags, however, experts used more domain-specific terms. We conclude that competitive games are not suited to elicit real expert-level descriptions. We also confirm that providing guidelines, based on conceptual frameworks that are more suited to moving images in a time-based fashion, could result in increasing the quality of the tags, thus allowing for creating more tag-based innovative services for online audiovisual heritage.

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Section: Related Workmentioning

confidence: 99%

Time‐based tags for fiction movies: comparing experts to novices using a video labeling game

Estrada

Hildebrand

Boer

et al. 2016

Asso for Info Science & Tech

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show abstract

“…Next, after analyzing the AAT (Art and Architecture Thesaurus) and the image categories suggested by Kim and Kim (2010a), Laine‐Hermandez and Westman (2008), and Yoon (2008), we included 17 subcategories, such as color, objects, people, and metadata elements, to the category framework; each category has one or more subcategories, as shown in Table 1.…”

Section: The Analysis Results Of Video Tagsmentioning

confidence: 99%

Toward video semantic search based on a structured folksonomy

Kim¹

2011

J. Am. Soc. Inf. Sci.

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This study investigated the effectiveness of query expansion using synonymous and co-occurrence tags in users' video searches as well as the effect of visual storyboard surrogates on users' relevance judgments when browsing videos. To do so, we designed a structured folksonomy-based system in which tag queries can be expanded via synonyms or co-occurrence words, based on the use of WordNet 2.1 synonyms and Flickr's related tags. To evaluate the structured folksonomy-based system, we conducted an experiment, the results of which suggest that the mean recall rate in the structured folksonomy-based system is statistically higher than that in a tag-based system without query expansion; however, the mean precision rate in the structured folksonomybased system is not statistically higher than that in the tag-based system. Next, we compared the precision rates of the proposed system with storyboards (SB), in which SB and text metadata are shown to users when they browse video search results, with those of the proposed system without SB, in which only text metadata are shown. Our result showed that browsing only text surrogates-including tags without multimedia surrogates-is not sufficient for users' relevance judgments.

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“…The key frames are fixed video images which include the most important video contents [3].The dynamic video summarization includes a sequence of small shots which have been placed in time order. Advantage of the dynamic method is that it keeps dynamically the video contents while key frame extraction has more flexibility to show the video contents and can be used as different pre-processors [4].…”

Section: Introductionmentioning

confidence: 99%

Video Summarization by an Innovative Method in Shot Detection

Ahmadzade

Farsi

2015

ELCVIA

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The aim of a video summarization system is to provide a set of key frames which contain the most important parts of video. This system results in efficient storage, quick browsing, and retrieval of collection of video. In this paper, we propose a new summarization system which firstly divides the video into meaningful shots using an innovative and fast method, and then we sample the video frames belonging to each shot which results in 97% reduction in under-process frames. Then, using various characteristics of sampled frames such as color histogram, correlation and moment of inertia, wepropose anadaptive aggregation function for combination of these characteristics (differences) and extraction of key frames. The proposed systemis evaluated using 250 manual key frames constructed by human operators from 50 downloaded videos. The obtained results show that the proposed system provides better results compared to 6 different traditional methods.

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Toward a conceptual framework of key‐frame extraction and storyboard display for video summarization

Cited by 12 publications

References 33 publications

Time‐based tags for fiction movies: comparing experts to novices using a video labeling game

Time‐based tags for fiction movies: comparing experts to novices using a video labeling game

Toward video semantic search based on a structured folksonomy

Video Summarization by an Innovative Method in Shot Detection

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