Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106096
Title: Video summarization via multiview representative selection
Authors: Meng, Jingjing
Wang, Suchen
Wang, Hongxing
Yuan, Junsong
Tan, Yap-Peng
Keywords: Video Summarization
Multi-view
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Meng, J., Wang, S., Wang, H., Yuan, J., & Tan, Y.-P. (2018). Video summarization via multiview representative selection. IEEE Transactions on Image Processing, 27(5), 2134-2145. doi:10.1109/TIP.2017.2789332
Series/Report no.: IEEE Transactions on Image Processing
Abstract: Video contents are inherently heterogeneous. To exploit different feature modalities in a diverse video collection for video summarization, we propose to formulate the task as a multiview representative selection problem. The goal is to select visual elements that are representative of a video consistently across different views (i.e., feature modalities). We present in this paper the multiview sparse dictionary selection with centroid co-regularization method, which optimizes the representative selection in each view, and enforces that the view-specific selections to be similar by regularizing them towards a consensus selection. We also introduce a diversity regularizer to favor a selection of diverse representatives. The problem can be efficiently solved by an alternating minimizing optimization with the fast iterative shrinkage thresholding algorithm. Experiments on synthetic data and benchmark video datasets validate the effectiveness of the proposed approach for video summarization, in comparison with other video summarization methods and representative selection methods such as K-medoids, sparse dictionary selection, and multiview clustering.
URI: https://hdl.handle.net/10356/106096
http://hdl.handle.net/10220/48870
ISSN: 1057-7149
DOI: 10.1109/TIP.2017.2789332
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2017.2789332
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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