RGBD Salient Object Detection: A Benchmark and Algorithms

  • Houwen Peng
  • Bing Li
  • Weihua Xiong
  • Weiming Hu
  • Rongrong Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

Abstract

Although depth information plays an important role in the human vision system, it is not yet well-explored in existing visual saliency computational models. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB saliency models can still be adequate in RGBD scenarios, we continue to provide a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency, the former one is estimated from existing RGB models while the latter one is based on the proposed multi-contextual contrast model. Moreover, a specialized multi-stage RGBD model is also proposed which takes account of both depth and appearance cues derived from low-level feature contrast, mid-level region grouping and high-level priors enhancement. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects from RGBD images, and also assign consistent saliency values for the target objects.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Microsoft Corp. Redmond WA. Kinect for Xbox 360Google Scholar
  2. 2.
    Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)Google Scholar
  3. 3.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  4. 4.
    Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR, pp. 73–80 (2010)Google Scholar
  5. 5.
    Borji, A., Itti, L.: Exploiting local and global patch rarities for saliency detection. In: CVPR, pp. 478–485 (2012)Google Scholar
  6. 6.
    Borji, A., Sihite, D.N., Itti, L.: Salient object detection: A benchmark. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 414–429. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Bruce, N.D.B., Tsotsos, J.K.: Saliency based on information maximization. In: NIPS (2005)Google Scholar
  8. 8.
    Carreira, J., Sminchisescu, C.: Cpmc: Automatic object segmentation using constrained parametric min-cuts. PAMI 34(7), 1312–1328 (2012)CrossRefGoogle Scholar
  9. 9.
    Chang, K.Y., Liu, T.L., Chen, H.T., Lai, S.H.: Fusing generic objectness and visual saliency for salient object detection. In: ICCV, pp. 914–921 (2011)Google Scholar
  10. 10.
    Cheng, M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)Google Scholar
  11. 11.
    Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: ICCV, pp. 1–8 (2013)Google Scholar
  12. 12.
    Ciptadi, A., Hermans, T., Rehg, J.M.: An in Depth View of Saliency. In: BMVC, pp. 1–11 (2013)Google Scholar
  13. 13.
    Desingh, K., Krishna, K.M., Jawahar, C.V., Rajan, D.: Depth really matters: Improving visual salient region detection with depth. In: BMVC, pp. 1–11 (2013)Google Scholar
  14. 14.
    Goferman, S., Manor, L.Z., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 1915–1926 (2010)Google Scholar
  15. 15.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)Google Scholar
  16. 16.
    Holz, D., Holzer, S., Rusu, R.B., Behnke, S.: Real-time plane segmentation using rgb-d cameras. In: RoboCup, pp. 306–317 (2011)Google Scholar
  17. 17.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  18. 18.
    Jia, Y., Han, M.: Category-independent object-level saliency detection. In: ICCV, pp. 1761–1768 (2013)Google Scholar
  19. 19.
    Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: ICCV, pp. 1665–1672 (2013)Google Scholar
  20. 20.
    Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: BMVC, pp. 1–12 (2011)Google Scholar
  21. 21.
    Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: A discriminative regional feature integration approach. In: CVPR, pp. 1–8 (2013)Google Scholar
  22. 22.
    Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by UFO: Uniqueness, focusness and objectness. In: ICCV, pp. 1976–1983 (2013)Google Scholar
  23. 23.
    Judd, T., Ehinger, K.A., Durand, F., Torralba, A.: Learning to predict where humans look. In: ICCV, pp. 2106–2113 (2009)Google Scholar
  24. 24.
    Koch, C., Ullman, S.: Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4(4), 219–227 (1985)Google Scholar
  25. 25.
    Lang, C., Nguyen, T.V., Katti, H., Yadati, K., Kankanhalli, M., Yan, S.: Depth matters: Influence of depth cues on visual saliency. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 101–115. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  26. 26.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph. 23(3), 689–694 (2004)CrossRefGoogle Scholar
  27. 27.
    Li, X., Li, Y., Shen, C., Dick, A.R., van den Hengel, A.: Contextual hypergraph modeling for salient object detection. In: ICCV, pp. 3328–3335 (2013)Google Scholar
  28. 28.
    Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV, pp. 2976–2983 (2013)Google Scholar
  29. 29.
    Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: CVPR, pp. 1–8 (2007)Google Scholar
  30. 30.
    Manen, S., Guillaumin, M., Gool, L.J.V.: Prime object proposals with randomized prim’s algorithm. In: ICCV, pp. 2536–2543 (2013)Google Scholar
  31. 31.
    Marchesotti, L., Cifarelli, C., Csurka, G.: A framework for visual saliency detection with applications to image thumbnailing. In: ICCV, pp. 2232–2239 (2009)Google Scholar
  32. 32.
    Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: CVPR, pp. 1139–1146 (2013)Google Scholar
  33. 33.
    Niu, Y., Geng, Y., Li, X., Liu, F.: Leveraging stereopsis for saliency analysis. In: CVPR, pp. 454–461 (2012)Google Scholar
  34. 34.
    Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: Contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012)Google Scholar
  35. 35.
    Prim, R.: Shortest connection networks and some generalizations. Bell System Tech. J., 1389–1401 (1957)Google Scholar
  36. 36.
    Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: CVPR, pp. 37–44 (2004)Google Scholar
  37. 37.
    Scharfenberger, C., Wong, A., Fergani, K., Zelek, J.S., Clausi, D.A.: Statistical textural distinctiveness for salient region detection in natural images. In: CVPR, pp. 979–986 (2013)Google Scholar
  38. 38.
    Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley (1992)Google Scholar
  39. 39.
    Sharma, G., Jurie, F., Schmid, C.: Discriminative spatial saliency for image classification. In: CVPR, pp. 3506–3513 (2012)Google Scholar
  40. 40.
    Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR, pp. 2296–2303 (2012)Google Scholar
  41. 41.
    Wang, P., Wang, J., Zeng, G., Feng, J., Zha, H., Li, S.: Salient object detection for searched web images via global saliency. In: CVPR, pp. 1–8 (2012)Google Scholar
  42. 42.
    Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  43. 43.
    Wolfe, J.M., Horowitz, T.S.: Opinion: What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5(6), 495–501 (2004)Google Scholar
  44. 44.
    Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155–1162 (2013)Google Scholar
  45. 45.
    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Houwen Peng
    • 1
  • Bing Li
    • 1
  • Weihua Xiong
    • 1
  • Weiming Hu
    • 1
  • Rongrong Ji
    • 2
  1. 1.Institute of AutomationChinese Academy of SciencesChina
  2. 2.Department of Cognitive ScienceXiamen UniversityChina

Personalised recommendations