Video summarization and scene detection by graph modeling

By Chong-Wah Ngo, Yufei Ma, HongJiang Zhang. IEEE transactions on circuits and systems for video technology (Print), 2005

359 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/284b13e5d9bd25f79ef953ee143f15a1a37693d9

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis (2015, positive): “[23] introduced a motion attention model to evaluate the importance of different scenes and frames, and utilized a temporal graph to hierarchically summarize videos from the scene level, cluster level, shot level up to subshots level.”
Video summarisation: A conceptual framework and survey of the state of the art (2008, background): “[46] measured the extent to which users rated video summaries in terms of enjoyability and informativeness, while Kopf et al.”
Color Image Segmentation Based on Mean Shift and Normalized Cuts (2007, methodology): “The Ncut method has been applied in video summarization, scene detection [17], and cluster-based image retrieval [18].”
Ranking Domain-Specific Highlights by Analyzing Edited Videos (2014, background): “There is a large literature on video summarization (see review [1]), including techniques based on keyframes [13,3,17,15].”
Video shot detection and condensed representation. a review (2006, methodology): “A different approach is presented by Ngo et al. [ 24 ], who create their hierarchical skimming based mostly on traditional concepts such as scenes and shots.”
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics (2020, methodology): “Many of these methods are used for the task of retrospectively classifying or recognizing events in videos [26, 56, 74].”
Multi-View Video Summarization (2010, methodology): “We extend the graph model for mono-view video summarization [10], [13], [14] and segmentation [38] to a spatio-temporal shot graph.”
Category-Specific Video Summarization (2014, neutral): “Most works [3,6,5,11] conduct user studies to compare diﬀerent summaries of the same video.”
Video Summarization With Attention-Based Encoder–Decoder Networks (2017, methodology): “from each keyshot. Video summarization has been studied over two decades [1]–[6]. During these years, many approaches have been developed by exploring cues ranging from low-level visual inconsistency [10] [11], attention [3] [5] [26], to high-level semantic change of concepts [6] [13] and entities i”
Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices (2015, background): “Object tracking and scene change detection: There are many existing tracking algorithms [60, 17] and scene change detection techniques [39, 63].”
Large-Scale Video Summarization Using Web-Image Priors (2013, background): “Most of the previous summarization techniques are designed for well-produced videos, and rely on low-level appearance and motion cues [22] [15].”