Scalable Video Summarization Using Skeleton Graph and Random Walk

Panda, Rameswar; Kuanar, Sanjay K.; Chowdhury, Ananda S.

doi:10.1109/icpr.2014.599

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…Without supervision, summarization methods must rely on low-level visual indices to determine the relevance of parts of a video. Various strategies have been studied, including clustering [1,9,16,38], interest prediction [31,17], and energy minimization [42,13]. Leveraging crawled web images is also another recent trend for video summarization [25,49,26].…”

Section: Related Workmentioning

confidence: 99%

Collaborative Summarization of Topic-Related Videos

Panda

Roy-Chowdhury

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Large collections of videos are grouped into clusters by a topic keyword, such as "Eiffel Tower" or "Surfing", with many important visual concepts repeating across them. Such a topically close set of videos have mutual influence on each other, which could be used to summarize one of them by exploiting information from others in the set. We build on this intuition to develop a novel approach to extract a summary that simultaneously captures both important particularities arising in the given video, as well as, generalities identified from the set of videos. The topic-related videos provide visual context to identify the important parts of the video being summarized. We achieve this by developing a collaborative sparse optimization method which can be efficiently solved by a half-quadratic minimization algorithm. Our work builds upon the idea of collaborative techniques from information retrieval and natural language processing, which typically use the attributes of other similar objects to predict the attribute of a given object. Experiments on two challenging and diverse datasets well demonstrate the efficacy of our approach over state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Collaborative Summarization of Topic-Related Videos

Panda

Roy-Chowdhury

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Without supervision, summarization methods rely on lowlevel visual indices to determine the important parts of a video. Various strategies have been studied, including clustering [12], [24], [29], [57], maximal biclique finding [7], interest prediction [46], [26], and energy minimization [61], [19]. Leveraging crawled web images or videos is also another recent trend for video summarization [32], [67], [33], [58].…”

Section: Related Workmentioning

confidence: 99%

Diversity-Aware Multi-Video Summarization

Panda

Mithun

Roy-Chowdhury

2017

IEEE Trans. on Image Process.

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Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos. We observe that each video in the collection may contain some information that other videos do not have, and thus exploring the underlying complementarity could be beneficial in creating a diverse informative summary. We develop a novel diversity-aware sparse optimization method for multi-video summarization by exploring the complementarity within the videos. Our approach extracts a multi-video summary, which is both interesting and representative in describing the whole video collection. To efficiently solve our optimization problem, we develop an alternating minimization algorithm that minimizes the overall objective function with respect to one video at a time while fixing the other videos. Moreover, we introduce a new benchmark data set, Tour20, that contains 140 videos with multiple manually created summaries, which were acquired in a controlled experiment. Finally, by extensive experiments on the new Tour20 data set and several other multi-view data sets, we show that the proposed approach clearly outperforms the state-of-the-art methods on the two problems-topic-oriented video summarization and multi-view video summarization in a camera network.

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“…(ii) [34] simply to find peaks in the curve of entropy and use histogram intersection to output final key frames, while the proposed method first selects the local peaks of entropy and then use density clustering to calculate the cluster centers as the final key frames. Density clustering [35] is the approach based on the local density of feature points, which is able to detect local clusters, while previous clustering approaches such as dynamic delaunay clustering [36], k-means clustering [37], spectral clustering [38] and graph clustering [39] cannot detect local clusters due to the fact that they only rely on the distance between feature points to do clustering.…”

Section: Introductionmentioning

confidence: 99%

Fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion

Tang

Liu

Wei³

et al. 2019

Neurocomputing

113

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Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface. Although great progress has been made recently, fast and robust hand gesture recognition remains an open problem, since the existing methods have not well balanced the performance and the efficiency simultaneously. To bridge it, this work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition. Moreover, a feature fusion strategy is also proposed to further improve feature representation, which elevates the performance of recognition. To validate our approach in a "wild" environment, we also introduce two new datasets called HandGesture and Action3D datasets. Experiments consistently demonstrate that our strategy achieves competitive results on Northwestern University, Cambridge, HandGesture and Action3D hand gesture datasets. Our code and datasets will release at https://github.com/Ha0Tang/HandGestureRecognition.

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Scalable Video Summarization Using Skeleton Graph and Random Walk

Cited by 19 publications

References 14 publications

Collaborative Summarization of Topic-Related Videos

Collaborative Summarization of Topic-Related Videos

Diversity-Aware Multi-Video Summarization

Fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion

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