Key frame extraction based on visual attention model

Lai, Jie-Ling; Yi, Yang

doi:10.1016/j.jvcir.2011.08.005

Cited by 86 publications

(47 citation statements)

References 13 publications

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“…Recently, the inclusion of perceptual metrics in the SBD and keyframe methods are gaining some space. Recently and in the context of 2D video, some key-frame extraction methods based on visual attention models have emerged as, [60][61][62][63]86]. However, for 3D video only two solutions are available [41,42].…”

Section: Sbd and Key-frame Extraction Methodsmentioning

confidence: 99%

Towards key-frame extraction methods for 3D video: a review

Ferreira

Cruz

Assunção

2016

J Image Video Proc.

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The increasing rate of creation and use of 3D video content leads to a pressing need for methods capable of lowering the cost of 3D video searching, browsing and indexing operations, with improved content selection performance. Video summarisation methods specifically tailored for 3D video content fulfil these requirements. This paper presents a review of the state-of-the-art of a crucial component of 3D video summarisation algorithms: the key-frame extraction methods. The methods reviewed cover 3D video key-frame extraction as well as shot boundary detection methods specific for use in 3D video. The performance metrics used to evaluate the key-frame extraction methods and the summaries derived from those key-frames are presented and discussed. The applications of these methods are also presented and discussed, followed by an exposition about current research challenges on 3D video summarisation methods.Keywords: 3D key-frames extraction, 3D video summarisation, Shot boundary detection ReviewIn the last years, new features have been implemented in video applications and terminal equipments due to users demand, who are always seeking for new viewing experiences more interactive and immersive, such as those provided by 3D video. This new visual experience is created by depth information that is part of 3D video and absent in classic 2D video. The inclusion of depth information in video signals is not a recent innovation, but the interest in this type of content and aspects related to it, such as acquisition, analysis, coding, transmission and visualisation, have been increasing recently [1,2]. Lately, 3D video has been attracting attention from industry, namely content producers, equipment providers, distributors and from the research community mostly on account of the improvements in Quality of Experience that it provides to viewers [3], as well as due to the new business opportunities presented by this emerging multimedia format. In the past, video repositories were relatively small so that indexing and retrieval operations were easy to perform. More recently, the massification of 3D video and its applications have resulted in the generation of huge amounts of data, increasing the need for methods that can efficiently index, search, browse and summarise the relevant information with minimum human intervention. Furthermore, 3D video description and management is also required to enable quick presentation of the most important information in a user-friendly manner [4,5]. Video summarisation is a video-content representation method that can fulfil these requirements. In contrast to summarisation of 2D video, which has been the subject of a significant amount of research, 3D video summarisation is still a relatively unexplored research problem which deserves more attention.A video summary is a short version of a full-length video that preserves the essential visual and semantic information of the original unabridged content. In the video summarisation process, a subset of key-frames or a set of shorter video...

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Section: Sbd and Key-frame Extraction Methodsmentioning

confidence: 99%

Towards key-frame extraction methods for 3D video: a review

Ferreira

Cruz

Assunção

2016

J Image Video Proc.

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“…J.L. Lai, et al [6] used a saliency-based visual attention model and selected the frames with maximum saliency value as key frames. M. Kumar, et al [7] analyzed the spatio-temporal information of the video by sparse representation and used a normalized clustering method to generate clusters, the middle frame in each temporal order-sorted cluster was selected as a key frame.…”

Section: Existing Key Frame Extraction Methodsmentioning

confidence: 99%

“…Methods in [5] and [6] are compared with our proposed algorithm in order to demonstrate the efficiency and accuracy of our work. Table 1 and Figure 6 show the quantitative test results in 5 video clips mentioned above, Figure 7 to Figure 11 partially illustrate the qualitative results in chronological order.…”

Section: Methodsmentioning

confidence: 99%

A Key Frame Extraction Algorithm Based on Clustering and Compressive Sensing

Pan

Shu

Zhang

2015

IJMUE

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“…Texture is also a commonly used low level feature. There are many techniques for texture extraction, mostly wavelet trans-form is used for texture analysis but in [14] Discrete Haar Wavelet Transforms is used whereas Daubechies wavelet transform s used in [11]. Motion is considered as one of the important features for capturing the visually interesting elements.…”

Section: )mentioning

confidence: 99%