• DocumentCode
    2570639
  • Title

    Salient region extraction based on intensity mapping for image retrieval

  • Author

    Congyan, Lang ; Xu De ; Ning, Li ; Songhe, Feng

  • Author_Institution
    Inst. of Comput. Sci. & Eng., Beijing Jiaotong Univ., Beijing, China
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    2177
  • Lastpage
    2180
  • Abstract
    Salient region extraction provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. In this paper, we propose a robust approach to extracting the salient region based on bottom-up visual attention. The main contributions are twofold: 1) Instead of the feature parallel integration, the proposed saliencies are derived by serial processing between texture and color feature. 2) A constructive approach is proposed for rendering an image by a non-linear intensity mapping, which can efficiently eliminate high contrast noise regions in the image. And then the salient map can be robustly generated for a variety of nature images. Finally, the salient region extracted by our algorithm is used for image semantic retrieval. Experiments show that the proposed algorithm can characterize the human perception well and achieve satisfied retrieval performance.
  • Keywords
    feature extraction; image denoising; image retrieval; image texture; adaptive content delivery; constructive approach; feature parallel integration; high contrast noise region elimination; human perception; image retrieval intensity mapping; image semantic retrieval; nonlinear intensity mapping; salient region extraction; serial processing; Biology computing; Colored noise; Computational modeling; Content based retrieval; Data mining; Humans; Image coding; Image generation; Image retrieval; Noise robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
  • Conference_Location
    San Antonio, TX
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4244-2793-2
  • Electronic_ISBN
    1062-922X
  • Type

    conf

  • DOI
    10.1109/ICSMC.2009.5346244
  • Filename
    5346244