HSCS : Hierarchical Sparsity Based Co-saliency Detection for RGBD Images

Runmin CONG, Jianjun LEI*, Huazhu FU, Qingming HUANG, Xiaochun CAO, Nam LING

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

82 Citations (Scopus)

Abstract

Co-saliency detection aims to discover common and salient objects in an image group containing more than two relevant images. Moreover, depth information has been demonstrated to be effective for many computer vision tasks. In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement. With the assistance of the intrasaliency map, the inter-image correspondence is formulated as a hierarchical sparsity reconstruction framework. The global sparsity reconstruction model with a ranking scheme focuses on capturing the global characteristics among the whole image group through a common foreground dictionary. The pairwise sparsity reconstruction model aims to explore the corresponding relationship between pairwise images through a set of pairwise dictionaries. In order to improve the intra-image smoothness and inter-image consistency, an energy function refinement model is proposed, which includes the unary data term, spatial smooth term, and holistic consistency term. Experiments on two RGBD co-saliency detection benchmarks demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively.

Original languageEnglish
Article number8556071
Pages (from-to)1660-1671
Number of pages12
JournalIEEE Transactions on Multimedia
Volume21
Issue number7
Early online date2 Dec 2018
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1999-2012 IEEE.

Keywords

  • Co-saliency detection
  • energy function refinement
  • global sparsity reconstruction
  • pairwise sparsity reconstruction
  • RGBD images

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