Video summarization from spatio-temporal features

Laganière, Robert; Bacco, Raphael; Hocevar, Arnaud; Lambert, Patrick; Païs, Grégory; Ionescu, Bogdan

doi:10.1145/1463563.1463590

Cited by 55 publications

(40 citation statements)

References 12 publications

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“…Video summarization can shorten video in several ways. In this paper, we focus on the two most common ones: keyframe selection, where the system identifies a series of defining frames [1,2,3,4,5] and key subshot selection, where the system identifies a series of defining subshots, each of which is a temporally contiguous set of frames spanning a short time interval [6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Video Summarization with Long Short-Term Memory

Zhang

Chao

Sha

et al. 2016

Lecture Notes in Computer Science

559

696

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Abstract. We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the task as a structured prediction problem, our main idea is to use Long Short-Term Memory (LSTM) to model the variable-range temporal dependency among video frames, so as to derive both representative and compact video summaries. The proposed model successfully accounts for the sequential structure crucial to generating meaningful video summaries, leading to state-of-the-art results on two benchmark datasets. In addition to advances in modeling techniques, we introduce a strategy to address the need for a large amount of annotated data for training complex learning approaches to summarization. There, our main idea is to exploit auxiliary annotated video summarization datasets, in spite of their heterogeneity in visual styles and contents. Specifically, we show that domain adaptation techniques can improve learning by reducing the discrepancies in the original datasets' statistical properties.

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Section: Introductionmentioning

confidence: 99%

Video Summarization with Long Short-Term Memory

Zhang

Chao

Sha

et al. 2016

Lecture Notes in Computer Science

559

696

View full text Add to dashboard Cite

show abstract

“…In (Laganière et al, 2008) the activity level is defined within a video as the number of pixels altering their characteristics between two images. As a consequence, he proposes to define an activity function by the number of detected STIPs within each frame.…”

Section: Principlementioning

confidence: 99%

Moving Object Analysis in Video Sequences Using Space-Time Interest Points

Simac-Lejeune¹

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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To cite this version:Alain Simac-Lejeune. Moving object analysis in video sequences using space-time interest points. Abstract: Among all the features which can be extracted from videos, we propose to use Space-Time Interest Points (STIPs). STIPs are particularly interesting because they are simple and robust low-level features providing an efficient characterization of moving objects within videos. In this paper, after defining STIPs and after giving some of their properties, we will use STIPs to detect moving objects and to characterize specific changes in the movements of these objects. Proposed results are obtained from two very different types of videos, namely athletic videos and animation movies. MOVING OBJECT ANALYSIS IN VIDEO SEQUENCES USING SPACE-TIME INTEREST POINTS

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“…Once shots had been selected for inclusion in the generated summary, sub-shots of 2 to 3 seconds were selected and some of them were played back at an accelerated rate. A storyboard of shot keyframes was generated and included at both the start, and the [12] end, of the generated summary. A smooth zoom from the opening storyboard to the playback window (occupying 80% of the screen) took place at the start of the summary, and as the summary transitioned from shot to shot this was tracked on the storyboard.…”

Section: Participants and Their Approachesmentioning

confidence: 99%

“…Finally, the University of Ottawa -SITE group in Canada combined with the Université de Savoie in France and a group from LAPI, University of Bucharest in Romania to study the spatio-temporal Figure 2: Example of Tukey-style boxplot activity levels of input videos [12]. This was done by generating a spatio-temporal matrix of interest points, with explicit removal of junk frames.…”

Section: Participants and Their Approachesmentioning

confidence: 99%

The trecvid 2008 BBC rushes summarization evaluation

Over

Smeaton

Awad

2008

Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

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This paper describes an evaluation of automatic video summarization systems run on rushes from several BBC dramatic series. It was carried out under the auspices of the TREC Video Retrieval Evaluation (TRECVid) as a followup to the 2007 video summarization workshop held at ACM Multimedia 2007. 31 research teams submitted video summaries of 40 individual rushes video files, aiming to compress out redundant and insignificant material. Each summary had a duration of at most 2% of the original. The output of a baseline system, which simply presented each full video at 50 times normal speed was contributed by Carnegie Mellon University (CMU) as a control.The 2007 procedures for developing ground truth lists of important segments from each video were applied at the National Institute of Standards and Technology (NIST) to the BBC videos. At Dublin City University (DCU) each summary was judged by 3 humans with respect to how much of the ground truth was included and how well-formed the summary was. Additional objective measures included: how long it took the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the summary's duration was. Assessor agreement on finding desired segments averaged 81%. Results indicated that while it was still difficult to exceed the performance of the baseline on in-1 cluding ground truth, the baseline was outperformed by most other systems with respect to avoiding redundancy/junk and presenting the summary with a pleasant tempo/rhythm.

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Video summarization from spatio-temporal features

Cited by 55 publications

References 12 publications

Video Summarization with Long Short-Term Memory

Video Summarization with Long Short-Term Memory

Moving Object Analysis in Video Sequences Using Space-Time Interest Points

The trecvid 2008 BBC rushes summarization evaluation

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