• DocumentCode
    595265
  • Title

    Visual saliency: A manifold way of perception

  • Author

    Hao Zhu ; Biao Han ; Xiang Ruan

  • Author_Institution
    Beijing Normal Univ., Beijing, China
  • fYear
    2012
  • fDate
    11-15 Nov. 2012
  • Firstpage
    2606
  • Lastpage
    2609
  • Abstract
    Visual saliency plays an important role in the human visual system HVS since it is indispensable for object detection and recognition. A bottom-up saliency model was proposed, following the manifold characteristic of HVS, previously developed for understanding HVS mechanism. The saliency of a given location of visual field is defined as the power of features responses after the dimensionality reduction with manifold learning for sparse representation of raw input. This saliency definition also explains the reason that HVS can suppress the response of redundant pattern and excite the response of attended pattern. Experiments show that our saliency model produces better predictions of human eye fixations on two dataset in the comparsion of four state-of-the-art methods.
  • Keywords
    eye; image coding; image representation; learning (artificial intelligence); object detection; object recognition; sparse matrices; visual perception; HVS manifold learning characteristic; attended pattern response excitation; bottom-up visual saliency model; dimensionality reduction; human eye fixation prediction; human visual system; object detection; object recognition; raw input sparse representation; redundant pattern response suppression; Abstracts; Biological information theory; Biological system modeling; Computational modeling; Encoding; Manifolds; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2012 21st International Conference on
  • Conference_Location
    Tsukuba
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4673-2216-4
  • Type

    conf

  • Filename
    6460701