①(School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China) ②(School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401, China)
In order to improve the applicability for different types of image and integrity of the results, a saliency detection algorithm is proposed. It combines the adaptive threshold merging with a new background selection strategy. In the segmentation process, the color difference sequence is obtained by the selective fusion of RGB and LAB of adjacent blocks. Adaptive threshold is generated by inverse proportion model of block area parameter. Merging progress is done after the adaptive threshold comparison with the color difference sequence. In the background selection process, background regions are obtained by the local relative position of background-subject-background in the local area. The experimental results are optimized for edge. Compared with other algorithms, the saliency map of two values obtained does not need external threshold algorithm in this paper. Adaptive threshold merging can eliminate the details of objects in complex environments and can focus on the saliency comparison of the same level size objects.
ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
LUO Huilan, WAN Chengtao, and KONG Fansheng. Salient region detection algorithm via KL divergence and multi-scale merging[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145.
[3]
WU Pohung, CHEN Chienchi, DING Jianjiun, et al. Salient region detection improved by principle component analysis and boundary information[J]. IEEE Transactions on Image Processing, 2013, 22(9): 3614-3624. doi: 10.1109/TIP.2013. 2266099.
[4]
SHETH C and VENKATESH R. Object saliency using a background prior[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 1931-1935. doi: 10.1109/ICASSP.2016.7472013.
[5]
LI G B and YU Y Z. Visual saliency based on multiscale deep features[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 5455-5463. doi: 10.1109/CVPR.2015.7299184.
[6]
ZHANG Wei, BORJI A, WANG Zhou, et al. The application of visual saliency models in objective image quality assessment: A statistical evaluation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(6): 1266-1278. doi: 10.1109/TNNLS.2015.2461603.
BI Duyan, KU Tao, ZHA Yufei, et al. Scale-adaptive object tracking based on color names histogram[J].Journal of Electronics & Information Technology, 2016, 38(5): 1099-1106. doi: 10.11999/JEIT150921.
[8]
XIANG D and ZHONG B J. Scale-space saliency detection in combined color space[C]. Chinese Automation Congress, Wuhan, China, 2015: 726-731. doi: 10.1109/CAC.2015. 7382593.
[9]
ACHANTA R, APPU S, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2281. doi: 10.1109/TPAMI.2012.120.
[10]
TONG Na, LU Huchuan, ZHANG Lihe, et al. Saliency detection with multi-scale superpixels[J]. IEEE Signal Processing Letters, 2014, 21(9): 1035-1039. doi: 10.1109/LSP. 2014.2323407.
ZHENG Ruilian, ZHONG Baojiang, and XU Dongsheng. Scale space shape analysis technique based on L curvature[J]. Journal of Nanjing University, 2012, 48(2): 172-181. doi: 10.13232/j.cnki.jnju.2012.02.007.
[12]
NGUYEN H T, WORRING M, and BOOMGAARD R V D. Watersnakes: Energy-driven watershed segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(3): 330-342. doi: 10.1109/TPAMI.2003. 1182096.
ZHU Yukun. Research on multi object detection algorithm based on local feature and spatial relation[D]. [Master dissertation], Shanghai Jiao Tong University, 2014: 3-76.
[14]
CHENG M M, ZHANG G X, MITRAN J, et al. Global contrast based salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011: 409-416. doi: 10.1109/TPAMI.2014. 2345401.
[15]
ZELNIK-MANOR L, TAL A, and MARGOLIN R. What makes a patch distinct?[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 1139-1146. doi: 10.1109/CVPR.2013.151.
[16]
CHENG M M, WARRELL L, LIN W Y, et al. Efficient salient region detection with soft image abstraction[C]. IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 1529-1536. doi: 10.1109/ICCV.2013.193.
