Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest Clustering

Kuanar, Sanjay K.; Ranga, K.; Chowdhury, Ananda S.

doi:10.1109/tmm.2015.2443558

Cited by 53 publications

(51 citation statements)

References 36 publications

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“…To address the challenges encountered in multi-view settings, there have been some specifically designed approaches that use random walk over spatio-temporal graphs [14] and rough sets [33] to summarize multi-view videos. A recent work in [28] uses bipartite matching constrained optimum path forest clustering to solve the problem of multi-view video summarization. An online method can also be found in [43].…”

Section: Related Workmentioning

confidence: 99%

“…To provide an objective comparison, we compare all the approaches using three quantitative measures, including Precision, Recall and F-measure ( 2×Precision×Recall Precision+Recall ) [14], [28]. For all these metrics, the higher value indicates better summarization quality.…”

Section: B Performance Measuresmentioning

confidence: 99%

“…We took an event as correctly detected if our framework produces a video shot between the start and end of the event. We follow the prior works [14], [28], [43] and consider an event to be redundant if we detect the event simultaneously from more than one camera. Such an evaluation setting gives a fair comparison with the previous state-of-the-art methods [14], [33], [28], [44], [45].…”

Section: B Performance Measuresmentioning

confidence: 99%

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Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Panda

Roy-Chowdhury

2017

IEEE Trans. Multimedia

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Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus they cannot fully exploit the complicated intra and inter-view correlations in summarizing multi-view videos in a camera network. In this paper, with the aim of summarizing multi-view videos, we introduce a novel unsupervised framework via joint embedding and sparse representative selection. The objective function is two-fold. The first is to capture the multiview correlations via an embedding, which helps in extracting a diverse set of representatives. The second is to use a 2,1norm to model the sparsity while selecting representative shots for the summary. We propose to jointly optimize both of the objectives, such that embedding can not only characterize the correlations, but also indicate the requirements of sparse representative selection. We present an efficient alternating algorithm based on half-quadratic minimization to solve the proposed non-smooth and non-convex objective with convergence analysis.A key advantage of the proposed approach with respect to the state-of-the-art is that it can summarize multi-view videos without assuming any prior correspondences/alignment between them, e.g., uncalibrated camera networks. Rigorous experiments on several multi-view datasets demonstrate that our approach clearly outperforms the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: B Performance Measuresmentioning

confidence: 99%

Section: B Performance Measuresmentioning

confidence: 99%

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Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Panda

Roy-Chowdhury

2017

IEEE Trans. Multimedia

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“…The result of the proposed method is an optimal video summary that maintain the diversity of the original video. Kuanar et al (2015) proposed a video summarization approach using a graph theoretic method. The steps can be summarized: Shot boundary detection is achieved depending on Bag of Visual Words and the global feature such as color, texture and shape to remove the redundant frames.…”

Section: Introductionmentioning

confidence: 99%

“…The steps can be summarized: Shot boundary detection is achieved depending on Bag of Visual Words and the global feature such as color, texture and shape to remove the redundant frames. The video summary is constructed using Gaussian entropy algorithm [18]. Sigari et al (2015) proposed a fast video summarization using an ondemand feature extraction and a fuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Al-Musawi¹,

Hasson²

2015

ijacsa

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Abstract-For monitoring public domains, surveillance camera systems are used. Reviewing and processing any subsequences from large amount of raw video streams is time and space consuming. Many efficient approaches of video summarization were proposed to reduce the amount of irrelevant information. Most of these approaches do not take into consideration the illumination or lighting changes that cause noise in video sequences. In this work, video summarization algorithm for video streams has been proposed using Histogram of Oriented Gradient and Correlation coefficients techniques. This algorithm has been applied on the proposed multi-model dataset which is created by combining the original data and the dynamic synthetic data. This dynamic data is proposed using Random Number Generator function. Experiments on this dataset showed the effectiveness of the proposed algorithm compared with traditional dataset.

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Enhanced Block‐Based Feature Agglomeration Clustering for Video Summarization

Sreeja

Kovoor

2021

Machine Learning Algorithms and Applications

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Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest Clustering

Cited by 53 publications

References 36 publications

Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Enhanced Block‐Based Feature Agglomeration Clustering for Video Summarization

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