Earticle

초록

영어

A novel automatic salient object detection algorithm, which integrates context-based saliency with location computation based on the boundary priors, is proposed. Input image is expressed as a close-loop graph with superpixels as nodes and salient object of image has a well-defined graph-based manifold ranking location. The saliency of the image elements is defined based on their relevances to the given seeds or queries. Saliency object location is carried out in a two-stage scheme to extract background regions and foreground salient objects efficiently. We introduce a location weight to measure the relationship of superpixels and the centroid of the detected salient regions to eliminate the background. Saliency map is computed through context analysis and location computing based on multi-scale superpixels. Experimental results on three public benchmark datasets demonstrate that our approach performs well compared to existing state-of-the-art methods.

목차

Abstract
 1. Introduction
 2. Graph-based Manifold Ranking Object Location
  2.1. Graph Construction and Manifold Ranking
  2.2. Salient Object Detection and Location
 3. Context and Location based Saliency Computation
 4. Experimental Results
  4.1. MSRA-1000
  4.2. MSRA-B
  4.3 ECSSD
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Duzhen Zhang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China, School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, PR China ]
• Chuancai Liu [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605