Skip to main content

Mining Cluster-Specific Knowledge for Saliency Ranking

  • Chapter
  • 1579 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8408))

Abstract

In this Chapter, we aim to explore how to mine the prior knowledge for various scene clusters. Moreover, we also propose to model the problem of visual saliency computation in a learning to rank framework, which is proved to be more effective than the classification and regression framework.

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

Buying options

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abdollahian, G., Pizlo, Z., Delp, E.: A study on the effect of camera motion on human visual attention. In: Proceedings of the 15th IEEE International Conference on Image Processing (ICIP), pp. 693–696 (2008), doi:10.1109/ICIP.2008.4711849

    Google Scholar 

  • Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Advances in Neural Information Processing Systems (NIPS), pp. 41–48 (2007)

    Google Scholar 

  • Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2008)

    Google Scholar 

  • Cerf, M., Harel, J., Einhauser, W., Koch, C.: Predicting human gaze using low-level saliency combined with face detection. In: Advances in Neural Information Processing Systems (NIPS), Vancouver, BC, Canada (2009)

    Google Scholar 

  • Chun, M.M.: Contextual guidance of visual attention. In: Itti, L., Rees, G., Tsotsos, J. (eds.) Neurobiology of Attention, 1st edn., pp. 246–250. Elsevier Press, Amsterdam (2005)

    Chapter  Google Scholar 

  • Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  • Evgeniou, T., Micchelli, C.A., Pontil, M.: Learning multiple tasks with kernel methods. Journal of Machine Learning Research 6, 615–637 (2005)

    MATH  MathSciNet  Google Scholar 

  • Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Maching Learning Research 4, 933–969 (2003)

    MathSciNet  Google Scholar 

  • Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Preceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008), doi:10.1109/CVPR.2008.4587715

    Google Scholar 

  • Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems (NIPS), pp. 545–552 (2007)

    Google Scholar 

  • Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: Preceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007), doi:10.1109/CVPR.2007.383267

    Google Scholar 

  • Itti, L.: Crcns data sharing: Eye movements during free-viewing of natural videos. In: Collaborative Research in Computational Neuroscience Annual Meeting, Los Angeles, California (2008)

    Google Scholar 

  • Itti, L., Baldi, P.: A principled approach to detecting surprising events in video. In: Preceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 631–637 (2005), doi:10.1109/CVPR.2005.40

    Google Scholar 

  • Itti, L., Koch, C.: Computational modeling of visual attention. Nature Review Neuroscience 2(3), 194–203 (2001)

    Article  Google Scholar 

  • 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), doi:10.1109/34.730558

    Article  Google Scholar 

  • Jacob, L., Bach, F., Vert, J.P.: Clustered multi-task learning: A convex formulation. In: Advances in Neural Information Processing Systems (NIPS), pp. 745–752 (2009)

    Google Scholar 

  • Joachims, T.: Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 217–226. ACM, New York (2006), doi:10.1145/1150402.1150429

    Google Scholar 

  • Kienzle, W., Wichmann, F.A., Scholkopf, B., Franz, M.O.: A nonparametric approach to bottom-up visual saliency. In: Advances in Neural Information Processing Systems (NIPS), pp. 689–696 (2007)

    Google Scholar 

  • Li, J., Tian, Y., Huang, T., Gao, W.: Multi-task rank learning for visual saliency estimation. IEEE Transactions on Circuits and Systems for Video Technology 21(5), 623–636 (2011), doi:10.1109/TCSVT.2011.2129430

    Article  Google Scholar 

  • Ma, Y.F., Hua, X.S., Lu, L., Zhang, H.J.: A generic framework of user attention model and its application in video summarization. IEEE Transactions on Multimedia 7(5), 907–919 (2005), doi:10.1109/TMM.2005.854410

    Article  Google Scholar 

  • Navalpakkam, V., Itti, L.: Search goal tunes visual features optimally. Neuron 53, 605–617 (2007)

    Article  Google Scholar 

  • Peters, R., Itti, L.: Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention. In: Preceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007), doi:10.1109/CVPR.2007.383337

    Google Scholar 

  • Pillonetto, G., Nicolao, G.D., Chierici, M., Cobelli, C.: Fast algorithms for nonparametric population modeling of large data sets. Automatica 45, 173–179 (2009)

    Article  MATH  Google Scholar 

  • Torralba, A.: Contextual influences on saliency. In: Itti, L., Rees, G., Tsotsos, J. (eds.) Neurobiology of Attention, 1st edn., pp. 586–592. Elsevier Press, Amsterdam (2005)

    Chapter  Google Scholar 

  • Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)

    Article  Google Scholar 

  • Watrous, R.: Learning algorithms for connectionist networks: Applied gradient methods of nonlinear optimization, pp. 619–627, San Diego, CA, USA (1987)

    Google Scholar 

  • Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, MULTIMEDIA 2006, pp. 815–824. ACM, New York (2006), doi:10.1145/1180639.1180824

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Li, J., Gao, W. (2014). Mining Cluster-Specific Knowledge for Saliency Ranking. In: Visual Saliency Computation. Lecture Notes in Computer Science, vol 8408. Springer, Cham. https://doi.org/10.1007/978-3-319-05642-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05642-5_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05641-8

  • Online ISBN: 978-3-319-05642-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics