Salient object detection via two-stage absorbing Markov chain based on background and foreground

Tang, W.; Wang, Zhijian; Zhai, Jiyou; Yang, Zai

doi:10.1016/j.jvcir.2019.102727

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Furthermore, the convex hull of feature points is utilized to predict foreground elements in [22] and then a smooth process is performed by minimizing a energy function. In addition, in [23], the convex hull is used to estimate the coarse foreground regions, thus computing the object distribution and the observation likelihood. Although several prior-based researches have been conducted to predict the region of interest quickly and accurately, there are still some problems to be solved.…”

Section: Introductionmentioning

confidence: 99%

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Zhi

Zhang

et al. 2020

Remote Sensing

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Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background prior. Secondly, we compute the prior probability of salient foreground regions and propose a weighting function to constrain false foreground clutter, gaining the foreground-based prediction map. Thirdly, we integrate the two prediction maps and improve the details of the integrated map by a guided filter function and a wake adjustment function, obtaining the fine selection of candidate regions. Afterwards, a classification is further performed to reduce false alarms and produce the final ship detection results. Qualitative and quantitative evaluations on two public available datasets demonstrate the robustness and efficiency of the proposed method against four advanced baseline methods.

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Section: Introductionmentioning

confidence: 99%

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Zhi

Zhang

et al. 2020

Remote Sensing

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Real-time salient object detection based on accuracy background and salient path source selection

Tsai,

Wang

2024

Vis Comput

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Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model

Mignotte

2023

Mathematics

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This work presents a Bayesian statistical approach to the saliency map estimation problem. More specifically, we formalize the saliency map estimation issue in the fully automatic Markovian framework. The major and original contribution of the proposed Bayesian–Markov model resides in the exploitation of a pixel pairwise modeling and a likelihood model based on a parametric mixture of two different class-conditional likelihood distributions whose parameters are adaptively and previously estimated for each image. This allows us to adapt our saliency estimation model to the specific characteristics of each image of the dataset and to provide a nearly parameter-free—hence dataset-independent—unsupervised saliency map estimation procedure. In our case, the parameters of the likelihood model are all estimated under the principles of the iterative conditional estimation framework. Once the estimation step is completed, the MPM (maximum posterior marginal) solution of the saliency map (which we show as particularly suitable for this type of estimation), is then estimated by a stochastic sampling scheme approximating the posterior distribution (whose parameters were previously estimated). This unsupervised data-driven Markovian framework overcomes the limitations of current ad hoc or supervised energy-based or Markovian models that often involve many parameters to adapt and that are finely tuned for each different benchmark database. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods and turns out to be particularly stable across a wide variety of benchmark datasets.

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Salient object detection via two-stage absorbing Markov chain based on background and foreground

Cited by 4 publications

References 18 publications

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Real-time salient object detection based on accuracy background and salient path source selection

Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model

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