The Visual Computer

, Volume 33, Issue 11, pp 1467–1482 | Cite as

A nonlocal \(L_{0}\) model with regression predictor for saliency detection and extension

  • Yiyang Wang
  • Risheng Liu
  • Xiaoliang Song
  • Zhixun Su
Original Article
  • 366 Downloads

Abstract

Estimating salient object regions automatically has enhanced many computer vision applications in recent years. By observing the intrinsic sparsity of saliency map, we propose a graph-based nonlocal \(L_{0}\) (NL\(L_{0}\)) minimization framework to extract its sparse geometric structure. Our experimental results demonstrate that our method with artificially designed control map yields a significant improvement compared with the state-of-the-art saliency detection methods on four publicly available data sets. These saliency maps are further used for content-aware image resizing and unsupervised matting to test their uniformity. Moreover, we propose to learn the control map adaptively from training data. This strategy totally differs from the previously designed one, which is verified to be effective on image-classified data set. NL\(L_{0}\) with data-driven strategy is extended to interactive segmentation task and is affirmed to be better-performed than other advanced interactive approaches.

Keywords

Nonlocal \(L_{0}\) Regression predictor Saliency detection Interactive segmentation 

Supplementary material

371_2016_1292_MOESM1_ESM.pdf (54.9 mb)
Supplementary material 1 (pdf 56241 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yiyang Wang
    • 1
  • Risheng Liu
    • 2
    • 3
  • Xiaoliang Song
    • 1
  • Zhixun Su
    • 1
  1. 1.School of Mathematical SciencesDalian University of TechnologyDalianChina
  2. 2.School of Software TechnologyDalian University of TechnologyDalianChina
  3. 3.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian University of TechnologyDalianChina

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