Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/633897
Title: Dense and Sparse Reconstruction Error Based Saliency Descriptor
Authors: Huchuan Lu;Xiaohui Li;Lihe Zhang;Xiang Ruan;Ming-Hsuan Yang
subject: region compactness|Saliency detection|Bayesian integration|context-based propagation|sparse representation|dense/sparse reconstruction error
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction error. The image boundaries are first extracted via superpixels as likely cues for background templates, from which dense and sparse appearance models are constructed. First, we compute dense and sparse reconstruction errors on the background templates for each image region. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, the pixel-level reconstruction error is computed by the integration of multi-scale reconstruction errors. Both the pixellevel dense and sparse reconstruction errors are then weighted by image compactness, which could more accurately detect saliency. In addition, we introduce a novel Bayesian integration method to combine saliency maps, which is applied to integrate the two saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against 24 state-of-the-art methods in terms of precision, recall, and F-measure on three public standard salient object detection databases.
URI: http://localhost/handle/Hannan/166867
http://localhost/handle/Hannan/633897
ISSN: 1057-7149
1941-0042
volume: 25
issue: 4
Appears in Collections:2016

Files in This Item:
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Title: Dense and Sparse Reconstruction Error Based Saliency Descriptor
Authors: Huchuan Lu;Xiaohui Li;Lihe Zhang;Xiang Ruan;Ming-Hsuan Yang
subject: region compactness|Saliency detection|Bayesian integration|context-based propagation|sparse representation|dense/sparse reconstruction error
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction error. The image boundaries are first extracted via superpixels as likely cues for background templates, from which dense and sparse appearance models are constructed. First, we compute dense and sparse reconstruction errors on the background templates for each image region. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, the pixel-level reconstruction error is computed by the integration of multi-scale reconstruction errors. Both the pixellevel dense and sparse reconstruction errors are then weighted by image compactness, which could more accurately detect saliency. In addition, we introduce a novel Bayesian integration method to combine saliency maps, which is applied to integrate the two saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against 24 state-of-the-art methods in terms of precision, recall, and F-measure on three public standard salient object detection databases.
URI: http://localhost/handle/Hannan/166867
http://localhost/handle/Hannan/633897
ISSN: 1057-7149
1941-0042
volume: 25
issue: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7396959.pdf6.97 MBAdobe PDFThumbnail
Preview File
Title: Dense and Sparse Reconstruction Error Based Saliency Descriptor
Authors: Huchuan Lu;Xiaohui Li;Lihe Zhang;Xiang Ruan;Ming-Hsuan Yang
subject: region compactness|Saliency detection|Bayesian integration|context-based propagation|sparse representation|dense/sparse reconstruction error
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction error. The image boundaries are first extracted via superpixels as likely cues for background templates, from which dense and sparse appearance models are constructed. First, we compute dense and sparse reconstruction errors on the background templates for each image region. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, the pixel-level reconstruction error is computed by the integration of multi-scale reconstruction errors. Both the pixellevel dense and sparse reconstruction errors are then weighted by image compactness, which could more accurately detect saliency. In addition, we introduce a novel Bayesian integration method to combine saliency maps, which is applied to integrate the two saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against 24 state-of-the-art methods in terms of precision, recall, and F-measure on three public standard salient object detection databases.
URI: http://localhost/handle/Hannan/166867
http://localhost/handle/Hannan/633897
ISSN: 1057-7149
1941-0042
volume: 25
issue: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7396959.pdf6.97 MBAdobe PDFThumbnail
Preview File