Segmentation guided local proposal fusion for co-saliency detection

Chung Chi Tsai, Xiaoning Qian, Yen Yu Lin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

9 Scopus citations


We address two issues hindering existing image co-saliency detection methods. First, it has been shown that object boundaries can help improve saliency detection; But segmentation may suffer from significant intra-object variations. Second, aggregating the strength of different saliency proposals via fusion helps saliency detection covering entire object areas; However, the optimal saliency proposal fusion often varies from region to region, and the fusion process may lead to blurred results. Object segmentation and region-wise proposal fusion are complementary to help address the two issues if we can develop a unified approach. Our proposed segmentation-guided locally adaptive proposal fusion is the first of such efforts for image co-saliency detection to the best of our knowledge. Specifically, it leverages both object-aware segmentation evidence and region-wise consensus among saliency proposals via solving a joint co-saliency and co-segmentation energy optimization problem over a graph. Our approach is evaluated on a benchmark dataset and compared to the state-of-the-art methods. Promising results demonstrate its effectiveness and superiority.
Original languageAmerican English
Title of host publication2017 IEEE International Conference on Multimedia and Expo (ICME)
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9781509060672
StatePublished - 28 Aug 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo


  • Adaptive fusion
  • Alternating optimization
  • Co-saliency
  • Co-segmentation
  • Energy minimization


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