Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/593291
Title: An Approach to Streaming Video Segmentation With Sub-Optimal Low-Rank Decomposition
Authors: Chenglong Li;Liang Lin;Wangmeng Zuo;Wenzhong Wang;Jin Tang
subject: Lowrank representation|spectral clustering|Video processing|Streaming segmentation
Year: 2016
Publisher: IEEE
Abstract: This paper investigates how to perform robust and efficient video segmentation while suppressing the effects of data noises and/or corruptions, and an effective approach is introduced to this end. First, a general algorithm, called sub-optimal low-rank decomposition (SOLD), is proposed to pursue the low-rank representation for video segmentation. Given the data matrix formed by supervoxel features of an observed video sequence, SOLD seeks a sub-optimal solution by making the matrix rank explicitly determined. In particular, the representation coefficient matrix with the fixed rank can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into SOLD based on the observation that small-size video patterns tend to recur frequently within the same object. Second, based on SOLD, we present an efficient inference algorithm to perform streaming video segmentation in both unsupervised and interactive scenarios. More specifically, the constrained normalized-cut algorithm is adopted by incorporating the low-rank representation with other low level cues and temporal consistent constraints for spatio-temporal segmentation. Extensive experiments on two public challenging data sets VSB100 and SegTrack suggest that our approach outperforms other video segmentation approaches in both accuracy and efficiency.
URI: http://localhost/handle/Hannan/175237
http://localhost/handle/Hannan/593291
ISSN: 1057-7149
1941-0042
volume: 25
issue: 5
Appears in Collections:2016

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Title: An Approach to Streaming Video Segmentation With Sub-Optimal Low-Rank Decomposition
Authors: Chenglong Li;Liang Lin;Wangmeng Zuo;Wenzhong Wang;Jin Tang
subject: Lowrank representation|spectral clustering|Video processing|Streaming segmentation
Year: 2016
Publisher: IEEE
Abstract: This paper investigates how to perform robust and efficient video segmentation while suppressing the effects of data noises and/or corruptions, and an effective approach is introduced to this end. First, a general algorithm, called sub-optimal low-rank decomposition (SOLD), is proposed to pursue the low-rank representation for video segmentation. Given the data matrix formed by supervoxel features of an observed video sequence, SOLD seeks a sub-optimal solution by making the matrix rank explicitly determined. In particular, the representation coefficient matrix with the fixed rank can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into SOLD based on the observation that small-size video patterns tend to recur frequently within the same object. Second, based on SOLD, we present an efficient inference algorithm to perform streaming video segmentation in both unsupervised and interactive scenarios. More specifically, the constrained normalized-cut algorithm is adopted by incorporating the low-rank representation with other low level cues and temporal consistent constraints for spatio-temporal segmentation. Extensive experiments on two public challenging data sets VSB100 and SegTrack suggest that our approach outperforms other video segmentation approaches in both accuracy and efficiency.
URI: http://localhost/handle/Hannan/175237
http://localhost/handle/Hannan/593291
ISSN: 1057-7149
1941-0042
volume: 25
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7423751.pdf4.23 MBAdobe PDFThumbnail
Preview File
Title: An Approach to Streaming Video Segmentation With Sub-Optimal Low-Rank Decomposition
Authors: Chenglong Li;Liang Lin;Wangmeng Zuo;Wenzhong Wang;Jin Tang
subject: Lowrank representation|spectral clustering|Video processing|Streaming segmentation
Year: 2016
Publisher: IEEE
Abstract: This paper investigates how to perform robust and efficient video segmentation while suppressing the effects of data noises and/or corruptions, and an effective approach is introduced to this end. First, a general algorithm, called sub-optimal low-rank decomposition (SOLD), is proposed to pursue the low-rank representation for video segmentation. Given the data matrix formed by supervoxel features of an observed video sequence, SOLD seeks a sub-optimal solution by making the matrix rank explicitly determined. In particular, the representation coefficient matrix with the fixed rank can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into SOLD based on the observation that small-size video patterns tend to recur frequently within the same object. Second, based on SOLD, we present an efficient inference algorithm to perform streaming video segmentation in both unsupervised and interactive scenarios. More specifically, the constrained normalized-cut algorithm is adopted by incorporating the low-rank representation with other low level cues and temporal consistent constraints for spatio-temporal segmentation. Extensive experiments on two public challenging data sets VSB100 and SegTrack suggest that our approach outperforms other video segmentation approaches in both accuracy and efficiency.
URI: http://localhost/handle/Hannan/175237
http://localhost/handle/Hannan/593291
ISSN: 1057-7149
1941-0042
volume: 25
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7423751.pdf4.23 MBAdobe PDFThumbnail
Preview File