An integration of bottom-up and top-down salient cues on RGB-D data: saliency from objectness versus non-objectness

Bottom-up and top-down visual cues are two types of information that helps the visual saliency models. These salient cues can be from spatial distributions of the features (space-based saliency) or contextual/task-dependent features (object-based saliency). Saliency models generally incorporate salient cues either in bottom-up or top-down norm separately. In this work, we combine bottom-up and top-down cues from both space- and object-based salient features on RGB-D data. In addition, we also investigated the ability of various pre-trained convolutional neural networks for extracting top-down saliency on color images based on the object dependent feature activation. We demonstrate that combining salient features from color and dept through bottom-up and top-down methods gives significant improvement on the salient object detection with space-based and object-based salient cues. RGB-D saliency integration framework yields promising results compared with the several state-of-the-art-models.