SCS: A Model of Saliency Detection Based on Deep Learning

doi:10.3969/j.issn.10062475.2018.04.010

Abstract

Abstract: In this paper, we propose a method of saliency detection based on deep learning. This method extracts the lowlevel contrast features and highlevel semantic features involved in the two visual attention mechanisms, and combines both of them to obtain a classificationbased saliency detection model SCS. Through the comparison experiment, it is concluded that the proposed detection model has significant advantages in the accuracy of saliency detection.

Key words: saliency, contrast feature, semantic feature, classification, detection model

CLC Number:

TP391

ZHANG Hongtao, LU Hongying, LIU Tengfei, ZHANG Lingyu, ZHANG Xiaoming. SCS: A Model of Saliency Detection Based on Deep Learning[J]. Computer and Modernization, doi: 10.3969/j.issn.10062475.2018.04.010.

References

［1］ Chen Liqun, Xie Xing, Fan Xin, et al. A visual attention model for adapting images on small displays［J］. Multimedia Systems, 2003,9(4):353364.
［2］ Hou Xiaodi, Zhang Liqing. Saliency detection: A spectral residual approach［C］// Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. 2007, doi: 10.1109/CVPR.2007.383267.
［3］ Christopoulos C, Skodras A, Ebrahimi T. The JPEG2000 still image coding system: An overview［J］. IEEE Transactions on Consumer Electronics, 2000,46(4):11031127.
［4］〖JP3〗Ko B C, Nam J Y. Automatic objectofinterest segmentation from natural images［C］// Proceedings of the 18th International Conference on Pattern Recognition. 2006,4:4548. 
［5］〖JP3〗Chen Tao, Cheng Mingming, Tan Ping, et al. Sketch2Photo: Internet image montage［J］. ACM Transactions on Graphics, 2009,28(5): Article No. 124. 
［6］ Perazzi F, Krhenbühl P, Pritch Y, et al. Saliency filters: Contrast based filtering for salient region detection［C］// Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012:733740.
［7］ Itti L, Koch C, Niebur E. A model of saliencybased visual attention for rapid scene analysis［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998,20(11):12541259.
［8］ Zhao Qi, Koch C. Learning a saliency map using fixated locations in natural scenes［J］. Journal of Vision, 2011,11(3):7476.
［9］ Borji A. Boosting bottomup and topdown visual features for saliency estimation［C］// Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012:438445.
［10］Han Biao, Zhu Hao, Ding Youdong. Bottomup saliency based on weighted sparse coding residual［C］// Proceedings of the 19th ACM International Conference on Multimedia. 2011:11171120.
［11］ Zhang Lingyun, Tong M H, Marks T K, et al. SUN: A Bayesian framework for saliency using natural statistics［J］. Journal of Vision, 2008,8(7), doi: 10.1167/8.7.32. 
［12］Han Junwei, He Sheng, Qian Xiaoliang, et al. An objectoriented visual saliency detection framework based on sparse coding representations［J］. IEEE Transactions on Circuits and Systems for Video Technology, 2013,23(12):20092021.
［13］Bruce N D B, Tsotsos J K. Saliency, attention, and visual search: An information theoretic approach［J］. Journal of Vision, 2009,9(3), doi: 10.1167/9.3.5.
［14］Borji A, Itti L. Exploiting local and global patch rarities for saliency detection［C］// Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012:478485.
［15］Judd T, Ehinger K, Durand F, et al. Learning to predict where humans look［C］// Proceedings of the 12th International Conference on Computer Vision. 2009:21062113.
［16］Cerf M, Harel J, Einhuser W, et al. Predicting human gaze using lowlevel saliency combined with face detection［C］// Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007:241248.
［17］Shen Chengyao, Song Mingli, Zhao Qi. Learning highlevel concepts by training a deep network on eye fixations［C］// Proceedings of the 2012 NIPS Workshop on Deep Learning and Unsupervised Feature Learning. 2012.
［18］Lin Yuetan, Kong Shu, Wang Donghui, et al. Saliency detection within a deep convolutional architecture［C］// Workshops at the 28th AAAI Conference on Artificial Intelligence. 2014:3137.
［19］Han Junwei, Zhang Dingwen, Wen Shifeng, et al. Twostage learning to predict human eye fixations via SDAEs［J］. IEEE Transactions on Cybernetics, 2016,46(2):487498.
［20］Li Hongyang, Chen Jiang, Lu Huchuan, et al. CNN for saliency detection with lowlevel feature integration［J］. Neurocomputing, 2017,226:212220.
［21］Bruce N D B, Tsotsos J K. Saliency based on information maximization［C］// Proceedings of the 18th International Conference on Neural Information Processing Systems. 2005:155162.
［22］Ramanathan S, Katti H, Sebe N, et al. An eye fixation database for saliency detection in images［C］// Proceedings of the 11th European Conference on Computer Vision. 2010:3043.
［23］Tatler B W, Baddeley R J, Gilchrist I D. Visual correlates of fixation selection: Effects of scale and time［J］. Vision Research, 2005,45(5):643659.
［24］GarciaDiaz A, FdezVidal X R, Pardo X M, et al. Saliency from hierarchical adaptation through decorrelation and variance normalization［J］. Image and Vision Computing, 2012,30(1):5164.
［25］Zhang Jianming, Sclaroff S. Saliency detection: A Boolean map approach［C］// Proceedings of the 2013 IEEE International Conference on Computer Vision. 2013:153160.
［26］Goferman S, ZelnikManor L, Tal A. Contextaware saliency detection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(10):19151926.
［27］ Hou Xiaodi, Harel J, Koch C. Image signature: Highlighting sparse salient regions［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(1):194201.

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