Local and Global Contrast for Saliency Estimate in DCT Domain

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Abstract:

In this paper, we present a new bottom-up visual saliency model, which utilizes local and global contrast method to calculate the saliency in DCT domain. Our proposed method is firstly used in the DCT domain. The local contrast method uses the center-surround operation to compute the local saliency, and the global contrast method calculate the dissimilarity between DCT blocks of image and any other DCT blocks in any location. The final saliency is generated by combining the local with global contrast saliency. Experimental evaluation on a publicly available benchmark dataset shows the proposed model can acquire state-of-the-art results and outperform the other models in terms of the ROC area.

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611-615

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October 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] L. Itti, C. Koch and E. Niebur: IEEE Trans. PAMI., (1998) no. 20, p.1254–1259.

Google Scholar

[2] Y. Ma and H. Zhang: Proceedings of the Eleventh ACM International Conference on Multimedia (Berkeley, USA, November 2-8, 2003), p.228–241.

Google Scholar

[3] M.M. Cheng, G. Zhang, N.J. Mitra, et al: IEEE Conference on Computer Vision and Pattern Recognition (Providence, USA, June 20-25, 2011), p.409–416.

Google Scholar

[4] M. Mancas, C.M. Thillou, B. Gosselin et al: IEEE International Conference on Image Processing (Atlanta, USA, Oct. 8-11 2006), pp.445-448.

Google Scholar

[5] J.J. Wu, Q. Fe, G.M. Shi, et al: Journal of Visual Communication and Image Representation, vol. 23 (2012) no. 7, pp.1158-1166.

Google Scholar

[6] X. Hou, L. Zhang: IEEE Conference on Computer Vision and Pattern Recognition (Minneapolis, USA, June 17-22, 2007), pp.1-8.

Google Scholar

[7] C.L. Guo, Q. Ma, L.M. Zhang: IEEE Conference on Computer Vision and Pattern Recognition (Anchorage, USA, June 23-28, 2008), pp.1-8.

Google Scholar

[8] N. Murray, M. Vanrell, X. Otazu, et al: IEEE Conference on Computer Vision and Pattern Recognition (Providence, USA, June 20-25, 2011), pp.433-440.

Google Scholar

[9] Q. Tian, N. Sebe, E. Loupias, et al: J. Electron. Imag., vol. 10 (2001) no. 4, p.835–849.

Google Scholar

[10] Y.M. Fang, Z. Chen, W. Lin, et al: IEEE Trans. Image Pro., vol. 21 (2012) no. 9, pp.3888-3901.

Google Scholar

[11] B. Schauerte, R. Stiefelhagen: IEEE Workshop on Applications of Computer Vision (Breckenridge, USA, Jan. 9-11, 2012), pp.137-144.

Google Scholar

[12] A. Oliva, A. Torralba, M.S. Castelhano, et al: IEEE Proceedings of the International Conference on Image Processing (USA, Sept. 14-17, 2003), p.253–256.

Google Scholar

[13] N.D.B. Bruce, J.K. Tsotsos: Proceeding of Advances in Neural Information Processing System (Vancouver, Canada, December 5-8, 2005, ), pp.155-162.

Google Scholar

[14] L. Zhang, M.H. Tong, T.K. Marks, et al: J VISION., (2008) no. 8, pp.1-20.

Google Scholar