Advertisement

Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1603–1610 | Cite as

Salient object detection via incorporating multiple manifold ranking

  • Yanzhao WangEmail author
  • Guohua Peng
Original Paper
  • 62 Downloads

Abstract

We propose a salient object detection method in this paper via incorporating multifeature-based boundary ranking and boundary connectivity ranking. For multifeature-based boundary ranking, Lab and Lab histogram features are chosen to construct the graph and the regions along each boundary are, respectively, ranked to other regions based on the newly defined graph to get four boundary-based saliency maps. Then, they are integrated to acquire the multifeature-based boundary ranking map. For multifeature-based boundary connectivity ranking, the graph is designed by Lab and spatial distances. The multifeature-based boundary connectivity ranking map is obtained by ranking the boundary connectivity based on the graph, where the boundary connectivity is averaged by adjacent regions and weighted by center prior. The saliency result is obtained by incorporating these two multifeature-based saliency maps with boundary connectivity. Compared with 14 existing models, the method in this paper can obtain preferable results on 3 frequently used datasets.

Keywords

Saliency Multifeature-based graph Boundary connectivity Manifold ranking 

Notes

References

  1. 1.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  2. 2.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision (ICCV), pp. 2106–2113. IEEE, Kyoto (2009)Google Scholar
  3. 3.
    Chang, K.Y., Liu, T.L., Chen, H.T., Lai, S.H.: Fusing generic objectness and visual saliency for salient object detection. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 914–921. IEEE, Barcelona (2011)Google Scholar
  4. 4.
    Annum, R., Riaz, M.M., Ghofoor, A.: Saliency detection using contrast enhancement and texture smoothing operations. Signal Image Video Process. 12(3), 505–511 (2018)CrossRefGoogle Scholar
  5. 5.
    Chan, K.: Saliency detection in video sequences using perceivable change encoded local pattern. Signal Image Video Process. 12(5), 975–982 (2018)CrossRefGoogle Scholar
  6. 6.
    Wang, L., Xue, J.R., Zheng, N.N., Hua, G.: Automatic salient object extraction with contextual cue. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 105–112. IEEE, Barcelona (2011)Google Scholar
  7. 7.
    Lu, Y., Zhang, W., Lu, H., Xue, X.: Salient object detection using concavity context. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 233–240. IEEE, Barcelona (2011)Google Scholar
  8. 8.
    Liu, T., Yuan, Z.J., Sun, J.A., Wang, J.D., Zheng, N.N., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRefGoogle Scholar
  9. 9.
    Cheng, M.M., Mitra, N.J., Huang, X.L., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)CrossRefGoogle Scholar
  10. 10.
    Nouri, F., Kazemi, K., Danyali, H.: Salient object detection using local, global and high contrast graphs. Signal Image Video Process. 12(4), 659–667 (2018)CrossRefGoogle Scholar
  11. 11.
    He, C., Chen, Z.X., Liu, C.Y.: Salient object detection via images frequency domain analyzing. Signal Image Video Process. 10(7), 1295–1302 (2016)CrossRefGoogle Scholar
  12. 12.
    Yang, J.M., Yang, M.H.: Top-down visual saliency via joint CRF and dictionary learning. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 576–588 (2017)CrossRefGoogle Scholar
  13. 13.
    Jiang, H.Z., Wang, J.D., Yuan, Z.J., Liu, T., Zheng, N.N., Li, S.P.: Automatic salient object segmentation based on context and shape prior. In: 2011 British Machine Vision Conference (BMVC). BMVA Press, Dundee (2011)Google Scholar
  14. 14.
    Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733–740. IEEE, Providence (2012)Google Scholar
  15. 15.
    Kim, J., Han, D., Tai, Y.W., Kim, J.: Salient region detection via high-dimensional color transform. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 883–890. IEEE, Columbus (2014)Google Scholar
  16. 16.
    Jiang, P., Ling, H.B., Yu, J.Y., Peng, J.L.: Salient region detection by UFO: uniqueness, focusness and objectness. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1976–1983. IEEE, Sydney (2013)Google Scholar
  17. 17.
    Wang, Y.Z., Peng, G.H., Zhou, M.: Saliency detection by hierarchically integrating compactness, contrast and boundary connectivity. Multimed. Tools Appl. 77(10), 11883–11901 (2018)CrossRefGoogle Scholar
  18. 18.
    Wei, Y.C., Wen, F., Zhu, W.J., Sun, J.: Geodesic saliency using background priors. In: 2012 European Conference on Computer Vision (ECCV), pp. 29–42. Springer, Florence (2012)CrossRefGoogle Scholar
  19. 19.
    Wang, J.P., Lu, H.C., Li, X.H., Tong, N., Liu, W.: Saliency detection via background and foreground seed selection. Neurocomputing 152, 359–368 (2015)CrossRefGoogle Scholar
  20. 20.
    Zhu, W.J., Liang, S., Wei, Y.C., Sun, J.: Saliency optimization from robust background detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2814–2821. IEEE, Columbus (2014)Google Scholar
  21. 21.
    Tong, N., Lu, H.C., Zhang, Y., Ruan, X.: Salient object detection via global and local cues. Pattern Recognit. 48(10), 3258–3267 (2015)CrossRefGoogle Scholar
  22. 22.
    Lu, H.C., Li, X.H., Zhang, L.H., Ruan, X., Yang, M.H.: Dense and sparse reconstruction error based saliency descriptor. IEEE Trans. Image Process. 25(4), 1592–1603 (2016)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Jiang, H.Z., Wang, J.D., Yuan, Z.J., Wu, Y., Zheng, N.N., Li, S.P.: Salient object detection: a discriminative regional feature integration approach. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2083–2090. IEEE, Portland (2013)Google Scholar
  24. 24.
    Tong, N., Lu, H.C., Ruan, X., Yang, M.H.: Salient object detection via bootstrap learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1884–1892. IEEE, Boston (2015)Google Scholar
  25. 25.
    Zhou, X.F., Liu, Z., Sun, G.L., Wang, X.Y.: Improving saliency detection via multiple kernel boosting and adaptive fusion. IEEE Signal Process. Lett. 23(4), 517–521 (2016)CrossRefGoogle Scholar
  26. 26.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  27. 27.
    Zhou, D.Y., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: 17th Annual Conference on Neural Information Processing Systems (NIPS), pp. 169–176. MIT Press, Canada (2004)Google Scholar
  28. 28.
    Yang, C., Zhang, L.H., Lu, H.C., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3166–3173. IEEE, Portland (2013)Google Scholar
  29. 29.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)CrossRefGoogle Scholar
  30. 30.
    Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: 2010 International Conference on Image Processing (ICIP), pp. 2653–2656. IEEE, Hong Kong (2010)Google Scholar
  31. 31.
    Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Minimum barrier salient object detection at 80 FPS. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1404–1412. IEEE, Santiago (2015)Google Scholar
  32. 32.
    Duan, L.J., Wu, C.P., Miao, J., Qing, L.Y., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 473–480. IEEE, Colorado (2011)Google Scholar
  33. 33.
    Zhou, L., Yang, Z.H., Yuan, Q., Zhou, Z.T., Hu, D.W.: Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans. Image Process. 24(11), 3308–3320 (2015)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Yan, Q., Xu, L., Shi, J.P., Jia, J.Y.: Hierarchical saliency detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1162. IEEE, Portland (2013)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Natural and Applied SciencesNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations