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By analysing 3D saliency in the case of RGB images and depth images, the class-conditional mutual information is computed for measuring the dependence of deep features extracted using a convolutional neural network; then, the posterior probability of the RGB-D saliency is formulated by applying Bayes\u2019 theorem. By assuming that deep features are Gaussian distributions, a discriminative mixed-membership naive Bayes (DMNB) model is used to calculate the final saliency map. The Gaussian distribution parameters can be estimated in the DMNB model by using a variational inference-based expectation maximization algorithm. The experimental results on RGB-D images from the NLPR dataset and NJU-DS400 dataset show that the proposed model performs better than other existing models.<\/jats:p>","DOI":"10.1186\/s41074-017-0037-0","type":"journal-article","created":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T11:57:48Z","timestamp":1515585468000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Visual saliency detection for RGB-D images under a Bayesian framework"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2203-1572","authenticated-orcid":false,"given":"Songtao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zhen","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Hanbing","family":"Qu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,1,10]]},"reference":[{"issue":"9","key":"37_CR1","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1109\/JPROC.2013.2265801","volume":"101","author":"P Le Callet","year":"2013","unstructured":"Le Callet P, Niebur E (2013) Visual attention and applications in multimedia technology. 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