RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map

WU Jianguo; SHAO Ting; LIU Zhengyi

doi:10.11999/JEIT161304

Volume 39 Issue 9

Sep. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2148-2154

WU Jianguo, SHAO Ting, LIU Zhengyi. RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2148-2154. doi: 10.11999/JEIT161304

Citation:

WU Jianguo, SHAO Ting, LIU Zhengyi. RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2148-2154. doi: 10.11999/JEIT161304

Citation:

PDF( 3478 KB)

RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map

doi: 10.11999/JEIT161304

Funds:

The National Key Technology RD Program (2015BAK24B00), The Specialized Research Fund for the Doctoral Program of Higher Education of China (20133401110009), Key Program of Natural Science Project of Educational Commission of Anhui Province (KJ2015A009), Open Funds of Co-Innovation Center for Information Supply Assurance Technology of Anhui University

Received Date: 2016-12-08
Rev Recd Date: 2017-05-22
Publish Date: 2017-09-19

Abstract

Abstract

Depth information is proved to be an important part of human vision. However, most saliency detection methods based on 2D images do not make good use of depth information, thus an effective saliency detection method for RGB-D image is presented. It extracts color feature combined with depth saliency feature and detects salient objects based on photographic composition prior and background prior. First, original depth map is preprocessed to form depth saliency feature by background vertex area, photographic composition intersections, and compactness method. Then the association matrix is constructed by the adjacency weight of comprehensive feature. Manifold ranking is running from foreground view to form foreground saliency map based on photographic composition prior and fusion of depth saliency feature and color feature. In order to correct the error caused by assumption, the boundary connectivity is used to suppress background from background view. Final saliency map builds on fusion of foreground and background saliency map. Experiments compared with 4 different methods on RGB-D1000 database show that the proposed method has better precision-recall curve and outperforms the state- of-the-art methods.
- Salient object detection,
- Depth saliency map,
- Multi-layer center rectangle,
- Manifold ranking,
- Photographic composition prior,
- Background prior

FullText(HTML)

References(23)

References

DING Y, XIAO J, and YU J. Importance filtering for image retargeting[C]. IEEE Conference on Computer Vision Pattern Recognition. Colorado Springs, CO, USA, 2011: 89-96. doi: 10.1109/CVPR.2011.5995445.

SIAGIAN C and ITTI L. Rapid biologically-inspired scene classification using features shared with visual attention[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2007, 29(2): 300-312. doi: 10.1109/TPAMI.2007. 40.

MAHADEVAN V and VASCONCELOS N. Saliency-based discriminant tracking[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Miami, FL, USA, 2009: 1007-1013. doi: 10.1109/CVPR.2009. 5206573.

ROTHER C, KOLMOGOROV V, and BLAKE A. GrabCut: Interactive foreground extraction using iterated graph cuts[J]. ACM Transactions on Graphics, 2004, 23(3): 309-314. doi: 10.1109/CGIV.2013.34.

LIN W Y, WU P C, and CHEN B R. Image retargeting using depth enhanced saliency[J]. Proceedings of 3DSA, 2013, 7:1.

FENG D, BARNES N, YOU S, et al. Local background enclosure for RGB-D salient object detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016: 2343-2350. doi: 10. 1109/CVPR.2016.257.

CHENG Y, FU H, WEI X, et al. Depth enhanced saliency detection method[J]. European Journal of Histochemistry, 2014, 55(1): 301-308. doi: 10.1145/2632856.2632866.

REN J, GONG X, YU L, et al. Exploiting global priors for RGB-D saliency detection[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Boston, MA, USA, 2015: 25-32. doi: 10.1109/ CVPRW.2015.7301391.

黄子超, 刘政怡. 特征融合与S-D概率矫正的RGB-D显著检测[J]. 中国图象图形学报, 2016, 21(10): 1392-1401.

HUANG Zichao and LIU Zhengyi. Feature integration and S-D probability correction based RGB-D saliency detection[J]. Journal of Image and Graphics, 2016, 21(10): 1392-1401.

GOFERMAN S, ZELNIK-MANOR L, and TAL A. Context- aware saliency detection[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2012, 34(10): 1915-1926. doi: 10.1109/TPAMI.2011.272.

王娇娇, 刘政怡. 多尺度构图先验的显著目标检测[J]. 中国图象图形学报, 2015, 20(12): 1664-1673.

WANG Jiaojiao and LIU Zhengyi. Multi-scale saliency detection based on composition prior[J]. Journal of Image and Graphics, 2015, 20(12): 1664-1673.

ZHOU L, YANG Z, YUAN Q, et al. Salient region detection via integrating diffusion-based compactness and local contrast[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3308-3320. doi: 10.1109/TIP.2015.2438546.

TANG Shuai, WANG Xiaoyu, LU Xutao, et al. Histogram of oriented normal vectors for object recognition with a depth sensor[C]. Asian Conference on Computer Vision 2012. Daejeon, Korea, 2012: 525-538. doi: 10.1007/978-3-642 -37444-9_41.

XUE H, GU Y, LI Y, et al. RGB-D saliency detection via mutual guided manifold ranking[C]. IEEE International Conference on Image Processing. Quebec City, QC, Canada, 2015: 666-670. doi: 10.1109/ICIP.2015.7350882.

YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]. IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2013: 3166-3173. doi: 10.1109/CVPR.2013.407.

ZHU W, LIANG S, WEI Y, et al. Saliency optimization from robust background detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014: 2814-2821. doi: 10.1109/CVPR. 2014.360.

QIN Y, LU H, XU Y, et al. Saliency detection via cellular automata[C]. IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015: 110-119. doi: 10.1109/CVPR.2015.7298606.

JIANG B, ZHANG L, LU H, et al. Saliency detection via absorbing Markov chain[C]. Proceedings of the IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013: 1665-1672. doi: 10.1109/ICCV.2013.209.

PENG H, LI B, XIONG W, et al. RGBD salient object detection: A benchmark and algorithms[C]. European Conference on Computer Vision. Zurich, Switzerland, 2014: 92-109. doi: 10.1007/978-3-319-10578-9_7.

JU R, GE L, GENG W, et al. Depth saliency based on anisotropic center-surround difference[C]. 2014 IEEE International Conference on Image Processing. Paris, France, 2014: 1115-1119. doi: 10.1109/ICIP.2014.7025222.

WEI Y, WEN F, ZHU W, et al. Geodesic saliency using background priors[C]. European Conference on Computer Vision. Florence, Italy, 2012: 29-42. doi: 10.1007/978-3-642- 33712-3_3.

Relative Articles

Supplements(0)

Cited By

Proportional views