关键词: 显著性目标检测, 深度学习, 复杂场景, 全卷积神经网络, 多特征融合

Abstract: Salient object detection aims at extracting visually salient objects in the image. It is an important task in computer vision and related researches. Considering that many existing algorithms based on deep learning suffer from insufficient feature learning and high detection error rate in complex natural scenes, this paper proposes a novel saliency object detection algorithm based on multi-feature fusion. The features of the saliency map predicted by HDHF (hybrid deep and handcrafted feature) model are used to fuse the deep features of the global pixel. In addition, the candidate nomination is applied to extract the position of candidate objects, and center priors are added to each candidate object. In the fully convolutional neural network, a forward propagation algorithm is utilized to finally predict the pixel-level salient object. Verification is performed on four image datasets with multiple salient objects and complex backgrounds. Experimental results demonstrate that the algorithm effectively improves the detection accuracy of salient objects in complex scenes, especially for the images with complex backgrounds.

Key words: salient object detection, deep learning, complex scene, fully convolutional neural network, multi-feature fusion