  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.

Computer Vision and Pattern Recognition