基于对比度与空间位置特征的显著性区域检测

doi:10.13229/j.cnki.jdxbgxb201505047

摘要/Abstract

摘要： 依据图像区域的对比度以及空间位置等先验视觉显著性知识,进行了自下而上、数据驱动的图像显著性区域检测。首先,提取图像中的前景区域,构造区域的对比度、空间位置特征函数,然后融合这些特征计算显著图。该算法将图像的空间关系与区域关系联系起来,得到了较精确的显著图。通过对国际上现有的公开数据集MSRA-1000的测试结果表明:本文算法可以抑制非显著区域干扰,显著图的一致性较高。同时,将本文算法的显著图应用于分割显著性区域,能够得到较好的分割效果。

关键词: 信息处理技术, 显著性检测, 显著区域, 图像对比度, 空间位置

Abstract: A bottom-up data driven algorithm is proposed to extract image salient region by utilizing visual saliency prior knowledge such as regional contrast and spatial location etc. In this algorithm, first, the foreground region in the image is extracted. Then, the regional contrast and spatial position feature functions are constructed. Finally, the saliency map is acquired by fusing these features. The model well establishes the relationship between the spatial space and regional contrast, and generates saliency maps with precise details. The results demonstrate that the proposed method consistently outperforms existing saliency detection algorithms, suppresses non-significant regional disturbance, and yields higher consistent saliency map, when evaluated using one of the large publicly available data sets MSRA-1000. Meanwhile, the extracted saliency map is applied to automatically segment saliency region and better segmentation results are obtained.

Key words: information processing, saliency detection, salient region, image contrast, spatial location

中图分类号:

TN911

张文杰, 熊庆宇. 基于对比度与空间位置特征的显著性区域检测[J]. 吉林大学学报(工学版), 2015, 45(5): 1709-1716.

ZHANG Wen-jie, XIONG Qing-yu. Image salient region detection algorithm based on contrast and spatial location[J]. 吉林大学学报(工学版), 2015, 45(5): 1709-1716.

参考文献

[1] Jiang H, Wang J, Yuan Z, et al. Salient object detection: a discriminative regional feature integration approach[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013: 2083-2090.
[2] Jian M W, Dong J Y, Ma J. Image retrieval using wavelet-based salient regions[J]. Imaging Science Journal, 2011, 59(4): 219-231.
[3] 李勇,陈贺新,耿晓东,等. 基于目标区域定位和特征融合的图像检索算法[J]. 吉林大学学报:工学版, 2008(增刊2):217-220. Li Yong, Chen He-xin, Geng Xiao-dong, et al. Image retrieval algorithm based on object region location and feature fusion[J]. Journal of Jilin University (Engineering and Technology Edition), 2008(Sup.2):217-220.
[4] Rutishauser U, Walther D, Koch C, et al. Is bottom-up attention useful for object recognition?[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, 2004: 37-44.
[5] Ko B C, Nam J. Object-of-interest image segmentation based on human attention and semantic region clustering[J]. JOSA A,2006, 23(10): 2462-2470.
[6] Yang J, Yang M H. Top-down visual saliency via joint crf and dictionary learning[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012: 2296-2303.
[7] Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 353-367.
[8] Itti L, Koch C, 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.
[9] Ma Y F, Zhang H J. Contrast-based image attention analysis by using fuzzy growing[C]∥Proceedings of the Eleventh ACM International Conference on Multimedia. ACM, New York, 2003: 374-381.
[10] Harel J, Koch C, Perona P. Graph-based visual saliency[C]∥Advances in Neural Information Processing System,Vancouver,Canada,2007:545-552.
[11] Hou X, Zhang L. Saliency detection: A spectral residual approach[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, 2007: 1-8.
[12] Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, 2009: 1597-1604.
[13] Stas Goferman, Lihi Zelnik-Manor, Ayellet Tal. Context-aware saliency detection[J]. IEEE Trans Pattern Anal Mach Intell,2012, 34(10): 1915-1926.
[14] 黄志勇,何发智,蔡贤涛,等.一种随机的视觉显著性检测算法[J].中国科学:信息科学,2011,41(7):863-874. Huang Zhi-yong, He Fa-zhi, Cai Xian-tao, et al. Efficient random saliency map detection[J]. Science China Information Sciences, 2011, 41(7): 863-874.
[15] 郭迎春,袁浩杰,吴鹏. 基于Local特征和Regional特征的图像显著性检测[J].自动化学报,2013,39(8):1214-1224. Guo Ying-Chun, Yuan Hao-Jie, WU Peng. Image saliency detection based on local and regional features[J]. Acta Automation Sinica,2013,39(8):1214-1224.
[16] Liu Z, Zou W, Li L, et al. Co-Saliency detection based on hierarchical segmentation[J]. IEEE Signal Processing Letters, 2014, 21(1):88-92.
[17] Fu H, Cao X, Tu Z. Cluster-based co-saliency detection[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2013, 22(10): 3766-3778.
[18] Xie Y, Lu H, Yang M. Bayesian saliency via low and mid level cues[J].Transactions on Image Processing, 2013,22(5):1689-1698.
[19] Cheng M M, Zhang G X, Mitra N J, et al. Global contrast based salient region detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, 2011: 409-416.
[20] Achanta R, Estrada F, Wils P, et al. Salient region detection and segmentation[C]∥6th International Conference on Computer Vision System,Santorini,Greece,2008:66-75.
[21] Ohashi T, Aghbari Z, Makinouchi A. Hill-climbing algorithm for efficient color-based image segmentation[C]∥IASTED International Conference on Signal Processing, Pattern Recognition, and Applications,Rhodes, 2003: 17-22.
[22] Li X, Lu H, Zhang L, et al. Saliency detection via dense and sparse reconstruction[C]∥IEEE International Conference on Computer Vision (ICCV), Sydney, 2013:2976-2983.
[23] Yang C, Zhang L, Lu H, et al. Saliency detection via graph-based manifold ranking[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013: 3166-3173.
[24] Lempitsky V, Kohli P, Rother C, et al. Image segmentation with a bounding box prior[C]∥IEEE International Conference on Computer Vision (ICCV), Kyoto, 2009: 277-284.
[25] Grady L, Jolly M P, Seitz A. Segmentation from a box[C]∥IEEE International Conference on Computer Vision (ICCV), Barcelona, 2011: 367-374.
[26] Einhäuser W, König P. Does luminance-contrast contribute to a saliency map for overt visual attention?[J]. European Journal of Neuroscience,2003, 17(5): 1089-1097.
[27] Judd T, Ehinger K, Durand F, et al. Learning to predict where humans look[C]∥IEEE 12th International Conference on Computer Vision, Kyoto, 2009: 2106-2113.
[28] 李茜. 图像的显著性特征提取[D]. 上海:上海大学通信与信息工程学院,2008. Li Qian. Saliency image feature extraction[D]. Shanghai:School of Communication and Information Engineering, Shanghai University, 2008.
[29] Achanta R, Susstrunk S. Saliency detection using maximum symmetric surround[C]∥the 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, 2010: 2653-2656.
[30] Zhai Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]∥Proceedings of the 14th Annual ACM International Conference on Multimedia. ACM, New York, 2006: 815-824.
[31] Han J, Ngan K N, Li M, et al. Unsupervised extraction of visual attention objects in color images[J]. IEEE Transactions on Circuits and Systems for Video Technology,2006, 16(1): 141-145.
[32] Rother C, Kolmogorov V, Blake A. Grabcut: interactive foreground extraction using iterated graph cuts[C]∥ACM Transactions on Graphics (TOG),ACM, New York, 2004:309-314.
[33] Boykov Y Y, Jolly M P. Interactive graph cuts for optimal boundary & region segmentation of objects in ND images[C]∥Proceedings of the Eighth IEEE International Conference on Computer Vision, Vancouver, 2001: 105-112.

相关文章 15

[1]	苏寒松,代志涛,刘高华,张倩芳. 结合吸收Markov链和流行排序的显著性区域检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1887-1894.
[2]	徐岩,孙美双. 基于卷积神经网络的水下图像增强方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1895-1903.
[3]	黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.
[4]	李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.
[5]	应欢,刘松华,唐博文,韩丽芳,周亮. 基于自适应释放策略的低开销确定性重放方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1917-1924.
[6]	陆智俊,钟超,吴敬玉. 星载合成孔径雷达图像小特征的准确分割方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1925-1930.
[7]	刘仲民,王阳,李战明,胡文瑾. 基于简单线性迭代聚类和快速最近邻区域合并的图像分割算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1931-1937.
[8]	单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[9]	姚海洋, 王海燕, 张之琛, 申晓红. 双Duffing振子逆向联合信号检测模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[10]	全薇, 郝晓明, 孙雅东, 柏葆华, 王禹亭. 基于实际眼结构的个性化投影式头盔物镜研制[J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[11]	陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[12]	陈涛, 崔岳寒, 郭立民. 适用于单快拍的多重信号分类改进算法[J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[13]	孟广伟, 李荣佳, 王欣, 周立明, 顾帅. 压电双材料界面裂纹的强度因子分析[J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[14]	林金花, 王延杰, 孙宏海. 改进的自适应特征细分方法及其对Catmull-Clark曲面的实时绘制[J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[15]	王柯, 刘富, 康冰, 霍彤彤, 周求湛. 基于沙蝎定位猎物的仿生震源定位方法[J]. 吉林大学学报(工学版), 2018, 48(2): 633-639.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed