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A Two-Stage Bayesian Integration Framework for Salient Object Detection on Light Field

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Abstract

Unique visual features of 4D light field data have been shown to affect detection of salient objects. Nevertheless, only a few studies explore it yet. In this study, several helpful visual features extracted from light field data are fused in a two-stage Bayesian integration framework for salient object detection. First, background weighted color contrast is computed in high dimensional color space, which is more distinctive to identify object of interest. Second, focusness map of foreground slice is estimated. Then, it is combined with the color contrast results via first-stage Bayesian fusion. Third, background weighted depth contrast is computed. Depth contrast has been proved to be an extremely useful cue for salient object detection and complementary to color contrast. Finally, in the second-stage Bayesian fusion step, the depth-induced contrast saliency is further fused with the first-stage saliency fusion results to get the final saliency map. Experiments of comparing with eight existing state-of-the-art methods on light field benchmark datasets show that the proposed method can handle challenging scenarios such as cluttered background, and achieves the most visually acceptable salient object detection results.

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References

  1. Schade U, Meinecke C (2011) Texture segmentation: do the processing units on the saliency map increase with eccentricity? Vis Res 51(1):1–12

    Article  Google Scholar 

  2. Ren Z, Gao S, Chia L-T, Tsang IWH (2014) Region-based saliency detection and its application in object recognition. IEEE Trans Circuits Syst Video Technol 24(5):769–779

    Article  Google Scholar 

  3. Zhu J-Y, Jiajun W, Yan X, Chang EIC, Tu Z (2015) Unsupervised object class discovery via saliency-guided multiple class learning. IEEE Trans Pattern Anal Mach Intell 37(4):862–875

    Article  Google Scholar 

  4. Chen Y, Pan Y, Song M, Wang M (2015) Image retargeting with a 3D saliency model. Signal Process 112:53–63

    Article  Google Scholar 

  5. Saha A, Wu QMJ (2013) A study on using spectral saliency detection approaches for image quality assessment. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2013, Vancouver, 26–31 May 2013, pp 1889–1893

  6. Sadaka NG, Karam LJ (2011) Efficient super-resolution driven by saliency selectivity. In: 18th IEEE international conference on image processing, ICIP 2011, Brussels, 11–14 Sept 2011, pp 1197–1200

  7. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: 2013 IEEE conference on computer vision and pattern recognition, Portland, 23–28 June 2013, pp 3586–3593

  8. Zhang C, Lin W, Li W, Zhou B, Xie J, Li J (2013) Improved image deblurring based on salient-region segmentation. Signal Process Image Commun 28(9):1171–1186

    Article  Google Scholar 

  9. Goferman S, Zelnik-Manor L, Tal A (2010) Context-aware saliency detection. In: The 23th IEEE conference on computer vision and pattern recognition, CVPR 2010, San Francisco, 13–18 June 2010, pp 2376–2383

  10. Cheng M-M, Mitra NJ, Huang X, Torr PHS, Hu S-M (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582

    Article  Google Scholar 

  11. Kim J, Han D, Tai Y-W, Kim J (2014) Salient region detection via high-dimensional color transform. In: 2014 IEEE conference on computer vision and pattern recognition, CVPR 2014, Columbus, 23–28 June 2014, pp 883–890

  12. Lin W, Sun MT, Li H, Chen Z, Li W, Zhou B (2012) Macroblock classification method for video applications involving motions. IEEE Trans Broadcast 58(1):34–46

    Article  Google Scholar 

  13. Han X, Li G, Lin W, Su X, Li H, Yang H, Wei H (2012) Periodic motion detection with ROI-based similarity measure and extrema-based reference-frame selection. In: Signal & information processing association annual summit and conference (APSIPA ASC), 2012 Asia-Pacific, pp 1–4

  14. Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: 2014 IEEE conference on computer vision and pattern recognition, CVPR 2014, Columbus, 23–28 June 2014, pp 2814–2821

  15. Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1265–1274

  16. Borji A, Sihite DN, Itti L (2012) Salient object detection: a benchmark. In: Computer vision–ECCV 2012: 12th European conference on computer vision, Florence, 7–13 Oct 2012, Proceedings, part II, pp 414–429

  17. Borji A, Cheng M-M, Jiang H, Li J (2015) Salient object detection: a benchmark. IEEE Trans Image Process 24(12):5706–5722

    Article  MathSciNet  Google Scholar 

  18. Jiang P, Ling H, Yu J, Peng J (2013) Salient region detection by UFO: uniqueness, focusness and objectness. In: IEEE international conference on computer vision, ICCV 2013, Sydney, 1–8 Dec 2013, pp 1976–1983

  19. Li N, Ye J, Ji Y, Ling H, Yu J (2014) Saliency detection on light field. In: The IEEE conference on computer vision and pattern recognition (CVPR)

  20. Li N, Sun B, Yu J (2015) A weighted sparse coding framework for saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5216–5223

  21. Niu Y, Geng Y, Li X, Liu F (2012) Leveraging stereopsis for saliency analysis. In: 2012 IEEE conference on computer vision and pattern recognition, Providence, 16–21 June 2012, pp 454–461

  22. Desingh K, Madhava Krishna K, Rajan D, Jawahar CV (2013) Depth really matters: improving visual salient region detection with depth. In: British machine vision conference, BMVC 2013, Bristol, 9–13 Sept 2013

  23. Peng H, Li B, Xiong W, Hu W, Ji R (2014) RGBD salient object detection: a benchmark and algorithms. In: Computer vision–ECCV 2014—13th European conference, Zurich, 6–12 Sept 2014, Proceedings, part III, pp 92–109

  24. Ren J, Gong X, Yu L, Zhou W, Yang MY (2015) Exploiting global priors for RGB-D saliency detection. In: 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 25–32

  25. Zhang J, Wang M, Gao J, Wang Y, Zhang X, Wu X (2015) Saliency detection with a deeper investigation of light field. In: Proceedings of the 24th international joint conference on artificial intelligence, IJCAI 2015, Buenos Aires, 25–31 July 2015, pp 2212–2218

  26. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  27. Xie Y, Lu H, Yang MH (2013) Bayesian saliency via low and mid level cues. IEEE Trans Image Process Publ IEEE Signal Process Soc 22(5):1689–1698

    MathSciNet  MATH  Google Scholar 

  28. Li X, Lu H, Zhang L, Ruan X, Yang M-H (2013) Saliency detection via dense and sparse reconstruction. In: IEEE international conference on computer vision, ICCV 2013, Sydney, 1–8 Dec 2013, pp 2976–2983

  29. Yang C, Zhang L, Lu H, Ruan X, Yang M-H (2013) Saliency detection via graph-based manifold ranking. In: 2013 IEEE conference on computer vision and pattern recognition, Portland, 23–28 June 2013, pp 3166–3173

  30. Wei Y, Wen F, Zhu W, Sun J (2012) Geodesic saliency using background priors. In: Computer vision– ECCV 2012—12th European conference on computer vision, Florence, 7–13 Oct 2012, Proceedings, part III, pp 29–42

  31. Perazzi F, Krähenbühl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE conference on computer vision and pattern recognition, Providence, 16–21 June 2012, pp 733–740

  32. Achanta R, Hemami SS, Estrada FJ,Süsstrunk S (2009) Frequency-tuned salient regiondetection. In: 2009 IEEE computer society conference on computervision and pattern recognition (CVPR 2009), Miami, 20–25 June 2009, pp 1597–1604

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Acknowledgements

We thank reviewers for valuable comments to improve the paper. This study in part is funded by The National Key Research and Development Program of China (Grants Nos. 2016YFB0700802, 2016YFB0800600), The National Natural Science Foundation of China (Grant No. 61305091), The Innovative Youth Projects of Ocean Remote Sensing Engineering Technology Research Center of State Oceanic Administration of China (Grant No. 2015001), and The Foundation of Sichuan Educational Committee (Grant No. 13ZB0103).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, Sichuan, China

    Anzhi Wang, Minghui Wang, Xiaoyan Li, Zetian Mi & Huan Zhou

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  1. Anzhi Wang
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  2. Minghui Wang
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  3. Xiaoyan Li
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Correspondence to Anzhi Wang.

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Wang, A., Wang, M., Li, X. et al. A Two-Stage Bayesian Integration Framework for Salient Object Detection on Light Field. Neural Process Lett 46, 1083–1094 (2017). https://doi.org/10.1007/s11063-017-9610-x

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