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access icon free Multi-scale contrast-based saliency enhancement for salient object detection

  • Author(s): Wenhui Zhou 1 ; Teng Song 1 ; Lili Lin 2 ; Andrew Lumsdaine 3
    • View affiliations
    • Affiliations: 1: School of Computer Science and Technology, Hangzhou Dianzi University, HangZhou 310018, People's Republic of China;
      2: School of Information and Electronic Engineering, Zhejiang Gongshang University, HangZhou 310018, People's Republic of China;
      3: Computer Science Department, Indianav University, Bloomington, IN 47405, USA
  • Source: Volume 8, Issue 3, June 2014, p. 207 – 215
    DOI:  10.1049/iet-cvi.2013.0118 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 13/05/2013, Accepted 21/08/2013, Revised 08/08/2013, Published 25/09/2013

To achieve more complete and more uniformly highlighted salient object regions, this study presents a computational saliency enhancement model that incorporates the properties of multi-scale and logarithmic response into the local and global contrasts. A distinct feature of the authors model is a novel saliency enhancement operator. This operator can effectively enhance the saliency of object interior regions while simultaneously reducing blur on object boundaries caused by multiple scales. Their model is a general one that can make flexible tradeoffs between precision and recall. Detailed comparisons with 12 state-of-the-art methods show that their method can obtain satisfactory salient object regions that are closer to the human-labelled results. In addition, their method provides superior results in precision–recall, F-measure and mean absolute error.

Inspec keywords: image restoration; object detection; image enhancement

Other keywords: multiscale contrast-based saliency enhancement operator; F-measure error; salient object detection; precision-recall error; object interior region; mean absolute error; logarithmic response property; blur reduction; human-labelled result; object boundary; computational saliency enhancement model

Subjects: Optical, image and video signal processing; Computer vision and image processing techniques

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