计算机科学与探索2019，Vol.13Issue(5)：834-845,12.DOI:10.3778/j.issn.1673-9418.1806027
基于多特征融合的显著性目标检测算法*
Salient Object Detection Algorithm Based on Multi-Feature Fusion*
摘要
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.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Key words
salient object detection/ deep learning/ complex scene/ fully convolutional neural network/ multi-feature fusion分类
信息技术与安全科学引用本文复制引用
张守东,杨明,胡太..基于多特征融合的显著性目标检测算法*[J].计算机科学与探索,2019,13(5):834-845,12.基金项目
The National Natural Science Foundation of China under Grant Nos. 61763029, 61873116 (国家自然科学基金). （国家自然科学基金）
