Salient Object Detection Method Based on Multi-scale Visual Perception Feature Fusion

Abstract

Salient object detection has important theoretical research significance and practical application value,and has played an important role in many computer vision applications,such as visual tracking,image segmentation and object recognition.How-ever,the unknown categories and variable scales of salient objects in natural environments are still a major challenge for salient object detection,which affects the detection results.Therefore,this paper proposes a salient object detection method based on multi-scale visual perception feature fusion.First,based on the characteristics of visual perception,multiple perceptual features are designed and extracted.Second,each perceptual feature adopts a multi-scale adaptive method to obtain feature saliency maps.Finally,each salient feature map is fused to obtain the final salient object.According to the characteristics of different image perception features,the proposed method adaptively extracts feature salient objects,and can adapt to changing detection objects and complex detection environments.Experimental results show that this method can effectively detect salient objects of unknown categories and different scales under the background interference of natural environment.