RGBD Co-saliency Detection via Bagging-Based Clustering

With the additional depth information, RGBD co-saliency detection, which is an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of RGBD images. This letter proposes a novel RGBD co-saliency model using bagging-based clustering. First, candida...

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Bibliographic Details
Published in:IEEE signal processing letters 2016-12, Vol.23 (12), p.1722-1726
Main Authors: Song, Hangke, Liu, Zhi, Xie, Yufeng, Wu, Lishan, Huang, Mengke
Format: Article
Language:English
Subjects:
Adaptation models
Bagging
Clustering
Clustering quality (CQ)
co-saliency detection
Color
Criteria
feature bagging
Feature extraction
Image color analysis
Image detection
Image quality
Optical wavelength conversion
Reliability
RGBD image
Signal processing
State of the art
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Summary:With the additional depth information, RGBD co-saliency detection, which is an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of RGBD images. This letter proposes a novel RGBD co-saliency model using bagging-based clustering. First, candidate object regions are generated based on RGBD single saliency maps and region pre-segmentation. Then, in order to make regional clustering more robust to different image sets, the feature bagging method is introduced to randomly generate multiple clustering results and the cluster-level weak co-saliency maps. Finally, a clustering quality (CQ) criterion is devised to adaptively integrate the weak co-saliency maps into the final co-saliency map for each image. Experimental results on a public RGBD co-saliency dataset show that the proposed co-saliency model significantly outperforms the state-of-the-art co-saliency models.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2016.2615293