全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

多尺度构图先验的显著目标检测

DOI: 10.11834/jig.20151211

Keywords: 显著目标检测,多尺度,构图先验,三分构图法,流行排序

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的针对基于对比度的显著检测方法,因忽略了特征的空间分布而导致准确性不高的问题,启发于边界先验关于图像空间布局的思想,提出构图先验的显著检测方法。方法假定目标分布于三分构图线周围,根据相关性比较计算显著值。首先,对图像进行多尺度超像素分割并构造闭环图;其次,提取构图线区域超像素特征并使用ManifoldRanking算法计算显著目标与背景的分布;然后,从目标和背景两个角度对显著值进行细化并利用像素区别性对像素点的显著值进行矫正;最后,融合多尺度显著值得到最终显著图。结果在公开的MSRA-1000、CSSD、ECSSD数据集上验证本文方法并与其他算法进行对比。本文方法在各数据集上准确率最高,分别为92.6%,89.2%,76.6%。且处理单幅图像平均时间为0.692s,和其他算法相比也有一定优势。结论人眼视觉倾向于在构图线周围寻找显著目标,构图先验是根据人眼注意机制研究显著性,具有合理性,且构图先验的方法提高了显著目标检测的准确性。

References

[1]  Koch C, Ullman S. Shifts in selective visual attention:towards the underlying neural circuitry[J]. Human Neurobiology, 1985, 4(4):219-227.
[2]  Itti L, Koch C, Neibur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Trans. on Patt. Anal. Mach. Intell., 1998, 20(11):1254-1259.
[3]  Cheng M M, Mitra N J, Huang X, et al. Global contrast based salient region detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):569-582.
[4]  Goferman S, Zelnik-Manor L, Tal A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(10):1915-1926.
[5]  Chang K Y, Liu T L, Chen H T, et al. Fusing generic objectness and visual saliency for salient object detection[C]//Proceedings of IEEE International Conference on Computer Vision. Barcelona:IEEE, 2011:914-921.
[6]  Achanta R, Estrada F, Wils P, et al. Salient Region Detection and Segmentation[M]//Computer Vision Systems. Berlin Heidelberg:Springer, 2008:66-75.
[7]  Jiang H, Wang J, Yuan Z, et al. Salient object detection:a discriminative regional feature integration approach[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR:IEEE, 2013:2083-2090.
[8]  Harel J, Koch C, Perona P. Graph-Based Visual Saliency[J]. Advances in Neural Information Processing Systems, 2006:545-552.
[9]  Wei Y, Wen F, Zhu W, et al. Geodesic saliency using background priors[M]//Computer Vision-ECCV 2012. Berlin Heidelberg:Springer, 2012:29-42.
[10]  Yang C, Zhang L, Lu H, et al. Saliency Detection via Graph-Based Manifold Ranking[J]. IEEE Conference on Computer Vision & Pattern Recognition, 2013, 9(4):3166-3173.
[11]  Jiang P, Ling H, Yu J, et al. Salient region detection by ufo:Uniqueness, focusness and objectness[C]//Proceedings of IEEE International Conference on Computer Vision. Sydney, NSW:IEEE, 2013:1976-1983.
[12]  Jiang H, Wang J, Yuan Z, et al. Automatic salient object segmentation based on context and shape prior[C]//British Machine Vision Conference. Dundee, scotland:BMVC Press, 2011, 110(1-12).[DOI:10.5244/C.25.110]
[13]  Achanta R, Shaji A, Smith K, et al. SLIC Superpixels (2010)[R]. EPFL Technical Report 149300.
[14]  Zhou D, Weston J, Gretton A, et al. Ranking on data manifolds[J]. Advances in Neural Information Processing Systems, 2004, 16:169-176.
[15]  Borji A, Sihite D N, Itti L. Salient object detection:a benchmark[M]//Computer Vision-ECCV 2012. Berlin Heidelberg:Springer, 2012, 414-429.
[16]  Yan Q, Xu L, Shi J, et al. Hierarchical saliency detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR:IEEE, 2013:1155-1162.
[17]  Jiang B, Zhang L, Lu H, et al. Saliency detection via absorbing markov chain[C]//Proceedings of IEEE International Conference on Computer Vision. Portland, OR:IEEE, 2013:1665-1672.
[18]  Margolin R, Tal A, Zelnik-Manor L. What makes a patch distinct?[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR:IEEE, 2013:1139-1146.
[19]  Perazzi F, Kr?henbühl P, Pritch Y, et al. Saliency filters:Contrast based filtering for salient region detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI:IEEE, 2012:733-740.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133