基于视觉中心转移的视觉显著性检测方法

doi:10.6040/j.issn.1672-3961.0.2016.340

摘要/Abstract

摘要： 针对现有许多检测方法提取出的显著性区域不够清晰的问题,提出一种基于视觉中心偏移的视觉显著性检测方法,在对图像进行预分割的基础上,结合图像的颜色对比特征、颜色分布特征和位置特征,提取出图像显著性区域,利用视觉中心转移模拟人类视野系统的视野转移过程,对图像进行多尺度分析,融合不同尺度显著图得到最终显著图。试验结果表明,本方法较现有显著性检测方法在视觉效果和查准率召回率有明显提高,ROC曲线下的面积可达0.952。

关键词: 多尺度分析, 图像分割, 显著图, 中心转移, 视觉显著性

Abstract: Many existing detection methods could not extract saliency regions clearly. A novel saliency detection method based on visual center offsetting was proposed. On the basis of images' pre-segmentation, combining with color contrast features, color distribution features and location features, saliency region of an image was extracted. The center offsetting was used to simulate the vision transfer process of human, after multi-scale analysis, by fusing saliency maps at different scales. The final saliency map was computed. The results showed that the performance of the proposed method was better on visual effect and the precision recall rate than existing methods, the area under ROC curve was 0.952.

Key words: saliency map, multi-scale, image segmentation, visual saliency, center shift

中图分类号:

TP391

胡金戈,唐雁. 基于视觉中心转移的视觉显著性检测方法[J]. 山东大学学报(工学版), 2017, 47(3): 27-33.

HU Jinge, TANG Yan. Visual saliency detection based on visual center shift[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 27-33.

参考文献

[1] GONZALEZ R C.数字图像处理[M].北京:电子工业出版社, 2006.
[2] JUDD Tilke, EHINGER Krista, DURAND Frédo, et al. Learning to predict where humans look[J]. Proceedings, 2009, 30(2):2106-2113.
[3] NAVALPAKKAM Vidhya, ITTI Laurent. An integrated model of top-down and bottom-up attention for optimizing detection speed[J]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, 2:2049-2056.
[4] FRITZ Gerald, SEIFERT Christin, PALETTA Lucas, et al. Attentive object detection using an information theoretic saliency measure[J]. Lecture Notes in Computer Science, 2004, 3368:29-41.
[5] BUTKO N J, MOVELLAN J R. Optimal scanning for faster object detection[C] //Computer Vision and Pattern Recognition. Losalamitos, CA, United States: Los Alamitos, 2009:2751-2758.
[6] HAN Sunhyoung, VASCONCELOS Nuno. Biologically plausible saliency mechanisms improve feedforward object recognition[J].Vision Research, 2010, 50(22):2295-2307.
[7] YAO Hong. TsaiHierarchical salient point selection for image retrieval[J].Pattern Recognition Letters, 2012, 33(33):1587-1593.
[8] WALTHER Dirk Koch Christof. Modeling attention to salient proto-objects[J]. Neural Networks the Official Journal of the International Neural Network Society, 2006, 19(9):1395-1407.
[9] LI Liang, JIANG Shuqiang, ZHA Zhengjun, et al. Partial-duplicate image retrieval via saliency-guided visual matching[J]. IEEE Multimedia, 2013, 20(3):13-23.
[10] BAI Xuefei, WANG Wenjian. Saliency-SVM: an automatic approach for image segmentation[J]. Neurocomputing, 2014, 136(8):243-255.
[11] GUO Mingwei, ZHAO Yuzhou, ZHANG Chenbin, et al. Fast object detection based on selective visual attention[J]. Neurocomputing, 2014, 144(1):184-197.
[12] ITTI Laurent, KOCH Christof, NIEBUR Ernst. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1998, 20(11):1254-1259.
[13] MA Yufei, ZHANG Hongjiang. Contrast-based image attention analysis by using fuzzy growing[C] //Eleventh ACM International Conference on Multimedia. New York, USA: ACM, 2003:374-381.
[14] ZHAI Yun, SHAH Mubarak. Visual attention detection in video sequences using spatiotemporal cues[C] //ACM International Conference on Multimedia. New York, USA: ACM, 2006:478-482.
[15] HOU Xiaodi, ZHANG Liqing. SALIENCY Detection: a spectral residual approach[C] // IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, USA: IEEE Computer Society, 2007:1-8.
[16] ZHANG Lingyun, TONG Matthew H, MARKS Tim K, et al. SUN: a bayesian framework for saliency using natural statistics[J]. Journal of Vision, 2008, 8(7):1-20.
[17] BRUCE Neil D B, TSOTSOS John K. Saliency, attention, and visual search: an information theoretic approach.[J]. Journal of Vision, 2009, 9(3):1-24.
[18] ACHANTA Radhakrishna, HEMAMI Sheila, ESTRADA Francisco, et al. Frequency-tuned salient region detection[C] //IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2009:1597-1604.
[19] GUO Chenlei, MA Qi, ZHANG Liming. Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform[C] //2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA:IEEE, 2008:1-8.
[20] ACHANTA Radhakrishna, SHAJI Appu, SMITH Kevin, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(11):2274-2282.
[21] TATLER Benjamin W. The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions[J]. Journal of Vision, 2007, 7(14):1-17.
[22] CHENG Mingming, MITRA Niloy J, HUANG Xiaolei, et al.Global contrast based salient region detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 37(3):409-416.
[23] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C] //International Conference on Computer Vision. Piscataway, USA: IEEE Computer Society, 2011:2564-2571.
[24] BORJI Ali, SIHITE Dicky N, ITTI Laurent. Salient Object Detection:A Benchmark[C] //European Conference on Computer Vision. Berlin:Springer-Verlag, 2012:414-429.
[25] FAWCETT Tom. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874.
[26] DAVIS Jesse, GOADRICH Mark. The relationship between precision-recall and ROC curves[C] //International Conference on Machine Learning.New York, USA: ACM, 2010:233-240.
[27] MURRAY Naila, VANRELL Maria, OTAZU Xavier, et al. Saliency estimation using a non-parametric low-level vision model[C] //IEEE Conference on Computer Vision & Pattern Recognition. Piscataway, NJ:IEEE, 2011:433-440.
[28] XIE Yulin, LU Huchuan, YANG Minghsuan. Bayesian saliency via low and mid level cues[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2013, 22(5):1689-1698.

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