JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (3): 27-33.doi: 10.6040/j.issn.1672-3961.0.2016.340

Previous Articles     Next Articles

Visual saliency detection based on visual center shift

HU Jinge, TANG Yan   

  1. College of Computer and Information Science, Southwest University, Chongqing 400715, China
  • Received:2016-08-27 Online:2017-06-20 Published:2016-08-27

PDF (PC)

452

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

CLC Number: 

  • TP391
[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.
[1] HU Jianping, LI Xin, XIE Qi, LI Ling, ZHANG Daochang. An unconstrained optimization EMD approach in 2D based on Delaunay triangulation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(5): 9-15.
[2] HUANG Jinchao. A new method for muti-objects image segmentation based on faster region proposal networks [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(4): 20-26.
[3] LI Lu, FAN Wentao, DU Jixiang. Brain MR image segmentation based on student's t mixture model with Markov random field [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 49-55.
[4] FAN Shuyan, DING Shifei. An improved multi-scale Graph cut algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(1): 28-33.
[5] LI Chunlei, ZHANG Zhaoxiang, LIU Zhoufeng, LIAO Liang, ZHAO Quanjun. A novel fabric defect detection algorithm based on textural differential visual saliency model [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(4): 1-8.
[6] YANG Xiulin1, HUANG Shuo2*, DENG Miao1, ZHANG Jihong1,3. Image fusion method based on saliency computation and adaptive PCNN [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(2): 35-42.
[7] YU Hai-jing1,2, LI Gui-ju1*. Color smoke image recognition based on differential box-counting fractal dimension algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(1): 35-40.
[8] QI Shile, WANG Meiqing. Adaptive active contour model for weak boundary extractio [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(6): 17-20.
[9] GUAN Yan, LI Cun-hua*, ZHONG Zhao-man, SUN Lan-lan. Segmentation algorithm of chemical molecular structure images [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(5): 65-70.
[10] ZHANG Feng1, LIANG Jun1, GAO Hong-mei2, PAN Meng3. Adaptive fault line selection method based on  a multi-scale frequency signal in non-solid earthed network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(2): 118-123.
[11] ZHANG Xin-ming, MAO Wen-tao, LI Zhen-yun. Two-dimensional Otsu image thresholding based on second order generalized probability [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(1): 25-33.
[12] WANG Liya, PAN Zhenkuan, WEI Weibo*, LIU Cunliang, ZHANG Zhimei, WANG Yu. Alternating convex relaxation minimization of the multiphase image
segmentation model and its Split Bregman algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(2): 40-45.
[13] WANG Xiu-fen, WANG Hui-yuan, WANG Song. Underwater object detection based on background subtraction and a saliency map [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(1): 12-16.
[14] WANG Xin-pei1, LIU Chang-chun1*, BAI Tong2. An image segmentation method based on mean divergence [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(4): 36-41.
[15] MA Zhi-qiang,CHANG Fa-liang,TIAN Wei,ZHAO Yao . An approach to face detection in color images [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(4): 19-22 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd