Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model
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 exploitati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/4/986 |
| _version_ | 1797619536556457984 |
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| author | Max Mignotte |
| author_facet | Max Mignotte |
| author_sort | Max Mignotte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:28:24Z |
| format | Article |
| id | doaj.art-7151268dd5364cdd9f4f165fcdf6b06f |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T08:28:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-7151268dd5364cdd9f4f165fcdf6b06f2023-11-16T21:56:47ZengMDPI AGMathematics2227-73902023-02-0111498610.3390/math11040986Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field ModelMax Mignotte0Vision Laboratory, Département d’Informatique et de Recherche Opérationnelle (DIRO), Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC H3C 3J7, CanadaThis 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.https://www.mdpi.com/2227-7390/11/4/986statistical estimationiterative conditional estimation (ICE)Markov random field (MRF)mode of posterior marginal (MPM)regions of interestsaliency map estimation |
| spellingShingle | Max Mignotte Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model Mathematics statistical estimation iterative conditional estimation (ICE) Markov random field (MRF) mode of posterior marginal (MPM) regions of interest saliency map estimation |
| title | Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model |
| title_full | Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model |
| title_fullStr | Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model |
| title_full_unstemmed | Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model |
| title_short | Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model |
| title_sort | saliency map estimation using a pixel pairwise based unsupervised markov random field model |
| topic | statistical estimation iterative conditional estimation (ICE) Markov random field (MRF) mode of posterior marginal (MPM) regions of interest saliency map estimation |
| url | https://www.mdpi.com/2227-7390/11/4/986 |
| work_keys_str_mv | AT maxmignotte saliencymapestimationusingapixelpairwisebasedunsupervisedmarkovrandomfieldmodel |