Skip to main content
SpringerLink
Log in
Book cover

International Conference on the Applications of Evolutionary Computation (Part of EvoStar)

EvoApplications 2019: Applications of Evolutionary Computation pp 308–324Cite as

Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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. 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. 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. 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_21

    Chapter  Google Scholar 

  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. 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_56

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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_41

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. Borji, A., Cheng, M., Jiang, H., Li, J.: Salient object detection: a survey. CoRR abs/1411.5878 (2014)

    Google Scholar 

  13. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  14. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)

    MATH  Google Scholar 

  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. 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. 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_4

    Chapter  Google Scholar 

  22. Lin, M., Zhang, C., Chen, Z.: Predicting salient object via multi-level features. Neurocomputing 205, 301–310 (2016)

    Article  Google Scholar 

  23. Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)

    Article  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Shima Afzali, Harith Al-Sahaf, Bing Xue, Christopher Hollitt & Mengjie Zhang

Authors
  1. Shima Afzali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harith Al-Sahaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bing Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christopher Hollitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shima Afzali .

Editor information

Editors and Affiliations

  1. University of Mainz, Mainz, Germany

    Paul Kaufmann

  2. University of Granada, Granada, Spain

    Pedro A. Castillo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Afzali, S., Al-Sahaf, H., Xue, B., Hollitt, C., Zhang, M. (2019). Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-16692-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16691-5

  • Online ISBN: 978-3-030-16692-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation