Advertisement

Genetic Programming for Feature Selection and Feature Combination in 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 11454)

Abstract

Salient Object Detection (SOD) aims to model human visual attention system to cope with the complex natural scene which contains various objects at different scales. Over the past two decades, a wide range of saliency features have been introduced in the SOD field, however feature selection has not been widely investigated for selecting informative, non-redundant, and complementary features from the existing features. In SOD, multi-level feature extraction and feature combination are two fundamental stages to compute the final saliency map. However, designing a good feature combination framework is a challenging task and requires domain-expert intervention. In this paper, we propose a genetic programming (GP) based method that is able to automatically select the complementary saliency features and generate mathematical function to combine those features. The performance of the proposed method is evaluated using four benchmark datasets and compared to nine state-of-the-art methods. The qualitative and quantitative results show that the proposed method significantly outperformed, or achieved comparable performance to, the competitor methods.

Keywords

Salient Object Detection Genetic programming Feature combination Feature selection 

References

  1. 1.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604. IEEE (2009)Google Scholar
  2. 2.
    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
  3. 3.
    Afzali, S., Al-Sahaf, H., Xue, B., Hollitt, C., Zhang, M.: Foreground and background feature fusion using a convex hull based center prior for salient object detection. In: Proceedings of the 33rd International Conference on Image and Vision Computing New Zealand, pp. 1–6. Springer (2018)Google Scholar
  4. 4.
    Afzali, S., Al-Sahaf, H., Xue, B., Hollitt, C., Zhang, M.: A genetic programming approach for constructing foreground and background saliency features for salient object detection. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 209–215. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03991-2_21CrossRefGoogle Scholar
  5. 5.
    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. IEEE (2017)Google Scholar
  6. 6.
    Ain, Q.U., Xue, B., Al-Sahaf, H., Zhang, M.: Genetic programming for feature selection and feature construction in skin cancer image classification. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018, Part I. LNCS (LNAI), vol. 11012, pp. 732–745. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-97304-3_56CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Al-Sahaf, H., Song, A., Neshatian, K., Zhang, M.: Two-tier genetic programming: towards raw pixel-based image classification. Expert Syst. Appl. 39(16), 12291–12301 (2012)CrossRefGoogle Scholar
  9. 9.
    Al-Sahaf, H., Xue, B., Zhang, M.: A multitree genetic programming representation for automatically evolving texture image descriptors. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 499–511. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68759-9_41CrossRefGoogle Scholar
  10. 10.
    Al-Sahaf, H., Zhang, M., Al-Sahaf, A., Johnston, M.: Keypoints detection and feature extraction: a dynamic genetic programming approach for evolving rotation-invariant texture image descriptors. IEEE Trans. Evol. Comput. 21(6), 825–844 (2017)CrossRefGoogle Scholar
  11. 11.
    Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Borji, A., Cheng, M., Jiang, H., Li, J.: Salient object detection: a survey. CoRR abs/1411.5878 (2014)Google Scholar
  13. 13.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  14. 14.
    Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)CrossRefGoogle Scholar
  15. 15.
    Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5300–5309. IEEE (2017)Google Scholar
  16. 16.
    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
  17. 17.
    Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2013)Google Scholar
  18. 18.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)zbMATHGoogle Scholar
  19. 19.
    Lavrenko, V., Allan, J., DeGuzman, E., LaFlamme, D., Pollard, V., Thomas, S.: Relevance models for topic detection and tracking. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 115–121. Morgan Kaufmann Publishers Inc. (2002)Google Scholar
  20. 20.
    Lee, G., Tai, Y.W., Kim, J.: Deep saliency with encoded low level distance map and high level features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 660–668 (2016)Google Scholar
  21. 21.
    Lensen, A., Al-Sahaf, H., Zhang, M., Xue, B.: Genetic programming for region detection, feature extraction, feature construction and classification in image data. In: Heywood, M.I., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds.) EuroGP 2016. LNCS, vol. 9594, pp. 51–67. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30668-1_4CrossRefGoogle Scholar
  22. 22.
    Lin, M., Zhang, C., Chen, Z.: Predicting salient object via multi-level features. Neurocomputing 205, 301–310 (2016)CrossRefGoogle Scholar
  23. 23.
    Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRefGoogle Scholar
  24. 24.
    Luo, Z., Mishra, A.K., Achkar, A., Eichel, J.A., Li, S., Jodoin, P.M.: Non-local deep features for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. vol. 2, p. 7 (2017)Google Scholar
  25. 25.
    Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–740. IEEE (2012)Google Scholar
  26. 26.
    Song, H., Liu, Z., Du, H., Sun, G., Le Meur, O., Ren, T.: Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE Trans. Image Process. 26(9), 4204–4216 (2017)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173. IEEE (2013)Google Scholar
  28. 28.
    Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 202–211 (2017)Google Scholar
  29. 29.
    Zhou, L., Yang, Z., Yuan, Q., Zhou, Z., Hu, D.: Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans. Image Process. 24(11), 3308–3320 (2015)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

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