Scalable gastroscopic video summarization via similar-inhibition dictionary selection

Wang, Shuai; Cong, Yang; Cao, Jiang; Yang, Yunsheng; Tang, Yandong; Zhao, Huaici; Yu, Haibin

doi:10.1016/j.artmed.2015.08.006

Cited by 33 publications

(12 citation statements)

References 62 publications

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“…Sooknanan et al [45] enhance and summarize the Nephrops Habitats depending on underwater videos. Actually, visual summarization is a hot topic in multimedia domain, which intends to extract key frames or video skims from long video sequence to achieve knowledge condensing and knowledge search, e.g., egocentric video summarization [46] , video summarization based on story driven [47] , video summarization depending on large-scale web image priors [48] , video summarization from consumer video [49] , video summarization via group sparsity [50,51,52] , multiview video summarization [53] and deep learning based video summarization [54] .…”

Section: Related Workmentioning

confidence: 99%

Novel event analysis for human-machine collaborative underwater exploration

Cong

Fan

et al. 2019

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Novel event analysis for human-machine collaborative underwater exploration

Cong

Fan

et al. 2019

Pattern Recognition

Self Cite

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“…Prior works [8,11] handle this issue by manually filtering redundant shots from the extracted summary which can be unreliable while summarizing large scale web videos. Recent works on sparse representative selection [62,58] also addresses this diversity problem by explicitly adding non-convex regularizers in the objective which makes it difficult to optimize.…”

Section: Collaborative Sparse Representative Selectionmentioning

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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“…With the rapid growing of the digital videos, e.g., an estimated 20 hours of videos are uploaded every minute to YouTube website , the video summarization technology [39] becomes much more important especially when content-based indexing and retrieval of video sequences has only seen limited success. Many prominent works have been proposed, such as mosaic-based video summarization [40], keyframes extraction via scene categorization [41]- [44], egocentric video summarization [45], story driven video summarization [46], large-scale video summarization via web image priors [47], joint video and image summarization [48], category-specific video summarization [49], dictionary learning based video summarization [50], consumer video summarization [51], group sparsity video summarization [18], [52] and also l 2,0 norm based dictionary selection for video summarization using SOMP [53]. General speaking, there are two sub-problems in video summarization: 1) keyframe extraction -extracting the most representative images from the underlying video sequence; 2) video skim generation -extracting a collection of video segments from the original video sequence, where each video skim itself is a video clip with a significantly shorter duration.…”

Section: B Selecting Keyframes For User Generated Videosmentioning

confidence: 99%

Adaptive Greedy Dictionary Selection for Web Media Summarization

Cong

Liu

Sun

et al. 2017

IEEE Trans. on Image Process.

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Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.

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Scalable gastroscopic video summarization via similar-inhibition dictionary selection

Cited by 33 publications

References 62 publications

Novel event analysis for human-machine collaborative underwater exploration

Novel event analysis for human-machine collaborative underwater exploration

Collaborative Summarization of Topic-Related Videos

Adaptive Greedy Dictionary Selection for Web Media Summarization

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