Skip to main content
SpringerLink
Log in

Saliency-Based Optimization for the Histogram of Oriented Gradients-Based Detection Methods

  • Conference paper
  • First Online:
Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

Included in the following conference series:

  • 1003 Accesses

Abstract

The paper presents research on using graphical saliency cue for optimizing computation of the distance metrics for HoG features. Saliency values have been computed in the area of a HoG descriptor for macro and micro scale. Macro scale uses HoG features as a global descriptor for an image presenting a particular object, whereas micro scale consists of feature points and k-nearest neighbours approach to create similarity measure. Mechanism has been tested on a chosen database consisting of 20 000 images. Promising results have been achieved for macro scale approach.

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

Access this chapter

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Institutional subscriptions

References

  1. Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: Proceedings of the International Conference on Image Processing, ICIP, pp. 2653–2656. Institute of Electrical and Electronics Engineers (IEEE), September 2010. http://dx.doi.org/10.1109/ICIP.2010.5652636

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). Similarity Matching in Computer Vision and Multimedia

    Article  Google Scholar 

  3. Cheng, M.M., Mitra, N.J., Huang, X., Hu, S.M.: Salientshape: group saliency in image collections. Vis. Comput. 30(4), 443–453 (2014). http://dx.doi.org/10.1007/s00371-013-0867-4

    Article  Google Scholar 

  4. Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015). http://dx.doi.org/10.1109/TPAMI.2014.2345401

    Article  Google Scholar 

  5. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.H.S.: Bing: binarized normed gradients for objectness estimation at 300fps. In: IEEE CVPR. Institute of Electrical & Electronics Engineers (IEEE), June 2014. http://dx.doi.org/10.1109/CVPR.2014.414

  6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. http://dx.doi.org/10.1109/CVPR.2005.177

  7. Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4), 11 (2013). http://dx.doi.org/10.1167/13.4.11

    Article  Google Scholar 

  8. Perazzi, F.: Saliency filters: contrast based filtering for salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 16–21 June 2012. http://ieeexplore.ieee.org/servlet/opac?punumber=6235193

  9. Gaurav, S., Frédéric, J., Cordelia, S.: Discriminative spatial saliency for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3506–3513 (2012). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6248093

  10. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.: The Amsterdam library of object images, January 2005. http://dx.doi.org/10.1023/B:VISI.0000042993.50813.60

  11. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. Institute of Electrical and Electronics Engineers (IEEE), June 2007. http://dx.doi.org/10.1109/CVPR.2007.383267

  12. Li, J., Tian, Y., Duan, L., Huang, T.: Estimating visual saliency through single image optimization. IEEE Signal Process. Lett. 20(9), 845–848 (2013). http://dx.doi.org/10.1109/LSP.2013.2268868

    Article  Google Scholar 

  13. Harel, J.: Graph-based visual saliency. In: Neural Information Processing Systems (NIPS), vol. 2006, pp. 545–552 (2006). https://papers.nips.cc/paper/3095-graph-based-visual-saliency.pdf

  14. Kadir, T., Brady, M.: Saliency, scale and image description. Int. J. Comput. Vis. 45(2), 83–105 (2001). http://dx.doi.org/10.1023/A:1012460413855

    Article  MATH  Google Scholar 

  15. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  16. Lowe, D.: Object recognition from local scale-invariant features (1999). http://dx.doi.org/10.1109/ICCV.1999.790410

  17. Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002). doi:10.1007/3-540-47969-4_9

    Chapter  Google Scholar 

  18. Ming, M.C., Guo, X.Z., Niloy, J.M., Xiaolei, H., Shi, M.H.: Global contrast based salient region detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409–416 (2011). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5995344

  19. Radhakrishna, A., Sheila, H., Francisco, E., Sabine, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597–1604 (2009). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5206596

  20. Valenti, R.: Image saliency by isocentric curvedness and color. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2185–2192 (2009). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5459240

  21. Rudoy, D., Goldman, D.B., Shechtman, E., Zelnik-Manor, L.: Learning video saliency from human gaze using candidate selection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1147–1154. Institute of Electrical and Electronics Engineers (IEEE), June 2013. http://dx.doi.org/10.1109/CVPR.2013.152

  22. Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 1–27 (2009). http://dx.doi.org/10.1167/9.12.15

    Article  Google Scholar 

  23. Shi, K., Wang, K., Lu, J., Lin, L.: Pisa: Pixelwise image saliency by aggregating complementary appearance contrast measures with spatial priors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2115–2122. Institute of Electrical and Electronics Engineers (IEEE), June 2013. http://dx.doi.org/10.1109/CVPR.2013.275

  24. Kadir, T.: Scale saliency: a novel approach to salient feature and scale selection. In: International Conference on Visual Information Engineering, VIE 2003 (2003)

    Google Scholar 

  25. Toet, A.: Computational versus psychophysical bottom-up image saliency: a comparative evaluation study. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2131–2146 (2011). http://dx.doi.org/10.1109/TPAMI.2011.53

    Article  Google Scholar 

  26. Wang, M., Konrad, J., Ishwar, P., Jing, K., Rowley, H.: Image saliency: from intrinsic to extrinsic context. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 417–424. Institute of Electrical and Electronics Engineers (IEEE), June 2011. http://dx.doi.org/10.1109/CVPR.2011.5995743

  27. Cui, X.: Temporal spectral residual: fast motion saliency detection. In: Proceedings of the 17th ACM International Conference on Multimedia (2009). http://dl.acm.org/citation.cfm?id=1631272

Download references

Author information

Authors and Affiliations

  1. Institute of Control and Industrial Electronics, Warsaw University of Technology, ul. Koszykowa 75, 00-662, Warsaw, Poland

    Grzegorz Kurzejamski & Marcin Iwanowski

Authors
  1. Grzegorz Kurzejamski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcin Iwanowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grzegorz Kurzejamski .

Editor information

Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Kurzejamski, G., Iwanowski, M. (2018). Saliency-Based Optimization for the Histogram of Oriented Gradients-Based Detection Methods. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59162-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation