Online video summarization on compressed domain

Almeida, Jurandy; Leite, Neucimar J.; Torres, Ricardo da S.

doi:10.1016/j.jvcir.2012.01.009

Cited by 55 publications

(46 citation statements)

References 37 publications

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“…A variety of pre-sampling techniques have been considered in prior works [25][26] [32]. Such approach is acceptable for other types of videos due to the inherent redundancy in video.…”

Section: Related Workmentioning

confidence: 99%

“…However, in the case of consumer video where the content tends to change abruptly and unpredictably, sampling at a predetermined rate [25] cannot guarantee the extraction of the best representative frames. Subsampling by selecting only Iframes [26] cannot ensure a viable set of representative frames either. This is due to the fact that, in general, no particular humanperception rules or video-summarization driven strategy are followed when coding video pictures as I-frame or B/P-frames.…”

Section: Related Workmentioning

confidence: 99%

“…This is due to the fact that, in general, no particular humanperception rules or video-summarization driven strategy are followed when coding video pictures as I-frame or B/P-frames. The video summarization based on compressed domain has a strong point of producing a video summary in a short time, which is potential for on-line applications [26]. However, creating a set of key frames while only a part of video available (for on-line application) cannot summarize the whole video content with a minimum number of key frames.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we evaluate the average processing time per frame, as appeared in [26] to evaluate the complexity. According to those experiments, our HIP-based technique takes 1.469 second on average to process a single frame, including 0.233 second per frame for the RPCA decomposition of the input signal into low rank and sparse components, and then solving the optimization problem (on average 1.236 second per frame).…”

Section: Evaluation and The Ground Truthmentioning

confidence: 99%

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RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

Dang

Radha

2015

IEEE Trans. on Image Process.

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Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video data set, without considering the underlying low-rank structure of the data set. Other algorithms exploit the low-rank component only, ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on robust principal component analysis (RPCA) is proposed. Furthermore, we target the challenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that the RPCA decomposes an input data into: 1) a low-rank component that reveals the systematic information across the elements of the data set and 2) a set of sparse components each of which containing distinct information about each element in the same data set. The two information types are combined into a single l1-norm-based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer and other types of videos. A comparison of the results obtained by our method with the ground truth and with related state-of-the-art algorithms clearly illustrates the viability of the proposed RPCA-based framework.

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“…A variety of pre-sampling techniques have been considered in prior works [25][26] [32]. Such approach is acceptable for other types of videos due to the inherent redundancy in video.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Evaluation and The Ground Truthmentioning

confidence: 99%

See 2 more Smart Citations

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

Dang

Radha

2015

IEEE Trans. on Image Process.

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“…It was also utilized for keyframe extraction, instead of parsing the full frame [15][16][17], or even for video summarization purpose [18,19]. For video retrieval, in [20] the DC-image was used to detect keyframes, then attention analysis is carried out on …”

Section: Introductionmentioning

confidence: 99%

DC-Image for Real Time Compressed Video Matching

Bekhet

Ahmed

Hunter

2014

Transactions on Engineering Technologies

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This chapter presents a suggested framework for video matching based on local features extracted directly from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that could be done to improve this computation complexity.

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Static Video Summarization through Optimum-Path Forest Clustering

Martins

Afonso

Osaku

et al. 2014

Advanced Information Systems Engineering

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This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.

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Online video summarization on compressed domain

Cited by 55 publications

References 37 publications

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

DC-Image for Real Time Compressed Video Matching

Static Video Summarization through Optimum-Path Forest Clustering

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