Skip to main content

A Novel Nonparametric Approach for Saliency Detection Using Multiple Features

  • Conference paper
  • 2645 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7197)

Abstract

This paper presents a novel saliency detection approach using multiple features. There are three types of features to be extracted from a local region around each pixel, including intensity, color and orientation. Principal Component Analysis(PCA) is employed to reduce the dimension of the generated feature vector and kernel density estimation is used to measure saliency. We compare our method with five classical methods on a publicly available data set. Experiments on human eye fixation data demonstrate that our method performs better than other methods.

Keywords

  • Saliency detection
  • Multiple features
  • PCA
  • Kernel density estimation

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Papageorgiou, C., Poggio, T.: A trainable system for object detection. International Journal of Computer Vision 38(1), 15–33 (2000)

    CrossRef  MATH  Google Scholar 

  2. Suh, B., Ling, H., Bederson, B.B., Jacobs, D.W.: Automatic thumbnail cropping and its effectiveness. In: Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology, Vancouver, Canada, pp. 95–104 (October 2003)

    Google Scholar 

  3. Rother, C., Bordeaux, L., Hamadi, Y., Blake, A.: AutoCollage. ACM Transactions on Graphics 25, 847–852 (2006)

    CrossRef  Google Scholar 

  4. Itti, L.: Automatic foveation for video compressing using a neurobiological model of visual attention. IEEE Transactions on Image Processing 13(10), 1304–1318 (2004)

    CrossRef  Google Scholar 

  5. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)

    CrossRef  Google Scholar 

  6. Treisman, A., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–138 (1980)

    CrossRef  Google Scholar 

  7. Bruce, N.D.B., Tsotsos, J.K.: Saliency based on information maximization. Advances in Neural Information Processing Systems 18, 155–162 (2006)

    Google Scholar 

  8. Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: a bayesian framework for saliency using natural statistics. Journal of Vision 8(7), 32–51 (2008)

    CrossRef  Google Scholar 

  9. Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8(7), 13–31 (2008)

    CrossRef  Google Scholar 

  10. Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. Journal of Vision 9(12), 15–41 (2009)

    CrossRef  Google Scholar 

  11. Kaganami, H.G., Ali, S.K., Zou, B.: Optimal Approach for Texture Analysis and Classification based on Wavelet Transform and Neural Network. Journal of Information Hiding and Multimedia Signal Processing 2(1), 33–40 (2011)

    CrossRef  Google Scholar 

  12. Liu, P., Jia, K.: Research and Optimization of Low-Complexity Motion Estimation Method Based on Visual Perception. Journal of Information Hiding and Multimedia Signal Processing 2(3), 33–40 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

He, X., Jing, H., Han, Q., Niu, X. (2012). A Novel Nonparametric Approach for Saliency Detection Using Multiple Features. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28490-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28490-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28489-2

  • Online ISBN: 978-3-642-28490-8

  • eBook Packages: Computer ScienceComputer Science (R0)