Embedding topological features into convolutional neural network salient object detection.

Salient object detection can be applied as a critical preprocessing step in many computer vision tasks. Recent studies of salient object detection mainly employed convolutional neural networks (CNNs) for mining high-level semantic properties. However, the existing methods can still be improved to find precise semantic information in different scenarios. In particular, in the two main methods employed for salient object detection, the patchwise detection models might ignore spatial structures among regions and the fully convolution-based models mainly consider semantic features in a global manner. In this paper, we proposed a salient object detection framework by embedding topological features into a deep neural network for extracting semantics. We segment the input image and compute weight for each region with low-level features. The weighted segmentation result is called a topological map and it provides an additional channel for the CNN to emphasize the structural integrity and locality during the extraction of semantic features. We also utilize the topological map for saliency refinement based on a conditional random field at the end of our model. Experimental results on six benchmark datasets demonstrated that our proposed framework achieves competitive performance compared to other state-of-the-art methods.