Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation

Runmin CONG, Jianjun LEI*, Huazhu FU, Qingming HUANG, Xiaochun CAO, Chunping HOU

*Corresponding author for this work

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

124 Citations (Scopus)

Abstract

Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely cross label propagation, is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model.

Original languageEnglish
Article number8070326
Pages (from-to)568-579
Number of pages12
JournalIEEE Transactions on Image Processing
Volume27
Issue number2
Early online date17 Oct 2017
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.

Keywords

  • Co-saliency detection
  • cross label propagation
  • feature matching
  • hybrid features
  • multi-constraint
  • RGBD images

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