Segmentation-enhanced saliency detection model based on distance transform and center bias

Hong Yun Gao, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Saliency detection is one of the extraordinary abilities of the human visual system (HVS), and also provides a powerful tool for predicting where humans tend to focus in the free-viewing process. In this paper, we propose a novel method for computing image saliency. At first, an image is subject to L0smoothing to characterize its fundamental constituents while diminishing insignificant details. Distance-transform-based saliency detection is then applied to the smoothed image, to extract the general salient regions and form a rough saliency map. Next, the segmentation information generated by normalized cuts is used to improve the saliency detection performance by averaging the saliency values in each segmented block. Finally, we employ the center-bias mechanism to further improve the saliency model. The proposed method is compared with six existing saliency models, and achieves the best performance in terms of the area under the ROC curve (AUC).
Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherIEEE
Pages2803-2807
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 4 May 20149 May 2014

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period4/05/149/05/14

Keywords

  • center bias
  • distance transform
  • image segmentation
  • Saliency detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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