RGBD Salient Object Detection: A Benchmark and Algorithms

Visual saliency detection for RGB-D images under a Bayesian framework

“…Depth-pooling models This type of model combines depth saliency maps and traditional 2D saliency maps simply to obtain saliency maps for RGB-D images [19][20][21][22]. Peng et al provided a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency: the former one is estimated from existing RGB models, whereas the latter one is based on the multi-contextual contrast model [19].…”

“…Peng et al provided a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency: the former one is estimated from existing RGB models, whereas the latter one is based on the multi-contextual contrast model [19]. Ren et al presented a two-stage 3D salient object detection framework, which first integrates the contrast region with the background, depth and orientation priors to achieve a saliency map and then reconstructs the saliency map globally [20].…”

“…Most existing approaches for 3D saliency detection either treat the depth feature as an indicator to weight the RGB saliency map [15][16][17][18] or consider the 3D saliency map as the fusion of saliency maps of these low-level features [19][20][21][22]. It is not clear how to integrate 2D saliency features with depth-induced saliency feature in a better way, and linearly combining the saliency maps produced by these features cannot guarantee better results.…”

“…Some other more complex combination algorithms have been proposed. These methods combine the depth-induced saliency map with the 2D saliency map either directly [19] or in a hierarchical way to calculate the final RGB-D saliency map [20]. However, because they are restricted by the computed saliency values, these saliency map combination methods are not able to correct incorrectly estimated salient regions.…”

“…From a pair of RGB and depth images, our model extracts deep features using a colour CNN and depth CNN, respectively, and performs saliency prediction using the DMNB model [14] within a Bayesian framework. In this work, we perform experiments based on the NLPR dataset in [19] distribution can be estimated in the DMNB model using a variational inference-based expectation maximization (EM) algorithm.…”