A Genetic Programming Approach for Constructing Foreground and Background Saliency Features for Salient Object Detection

  • Shima AfzaliEmail author
  • Harith Al-Sahaf
  • Bing Xue
  • Christopher Hollitt
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)


Salient Object Detection (SOD) methods have been widely investigated in order to mimic human visual system in selecting regions of interest from complex scenes. The majority of existing SOD methods have focused on designing and combining handcrafted features. This process relies on domain knowledge and expertise and becomes increasingly difficult as the complexity of candidate models increases. In this paper, we develop an automatic feature combination method for saliency features to relieve human intervention and domain knowledge. The proposed method contains three phases, two Genetic Programming (GP) phases to construct foreground and background features and a spatial blending phase to combine those features. The foreground and background features are constructed to complement each other, therefore one can improve other’s shortcomings. This method is compared with the state-of-the-art methods on four different benchmark datasets. The results indicate the new automatic method is comparable with the state-of-the-art methods and even improves SOD performance on some datasets.


Salient object detection Foreground Background Genetic programming 


  1. 1.
    Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: Proceedings of the 17th IEEE International Conference on Image Processing, pp. 2653–2656. IEEE (2010)Google Scholar
  2. 2.
    Afzali, S., Xue, B., Al-Sahaf, H., Zhang, M.: A supervised feature weighting method for salient object detection using particle swarm optimization. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 1–8 (2017)Google Scholar
  3. 3.
    Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M., Zhang, M.: Automatically evolving rotation-invariant texture image descriptors by genetic programming. IEEE Trans. Evol. Comput. 21(1), 83–101 (2017)Google Scholar
  4. 4.
    Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a survey. arXiv preprint arXiv:1411.5878, pp. 1–26 (2014)
  5. 5.
    Koza, J.R.: Genetic Programming (1997)Google Scholar
  6. 6.
    Lin, M., Zhang, C., Chen, Z.: Predicting salient object via multi-level features. Neurocomputing 205, 301–310 (2016)CrossRefGoogle Scholar
  7. 7.
    Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRefGoogle Scholar
  8. 8.
    Smith, S.M., Brady, J.M.: SUSANA new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)CrossRefGoogle Scholar
  9. 9.
    Yang, C., Zhang, L., Lu, H.: Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process. Lett. 20(7), 637–640 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shima Afzali
    • 1
    Email author
  • Harith Al-Sahaf
    • 1
  • Bing Xue
    • 1
  • Christopher Hollitt
    • 1
  • Mengjie Zhang
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations