基于多尺度视觉感知特征融合的显著目标检测方法

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

显著性物体检测具有重要的理论研究意义和实际应用价值,已在许多计算机视觉应用中发挥了重要作用,如视觉追踪、图像分割、物体识别等。然而,自然环境下显著目标的类别未知、尺度多变依然是物体检测面临的一大挑战,影响着显著目标的检测效果。因此,提出了一种基于多尺度视觉感知特征融合的显著目标检测方法。首先,基于视觉感知显著目标的特性,设计并提取多个图像感知特征。其次,图像感知特征采用多尺度自适应方式,获取特征显著图。然后,将各个显著特征图融合,获得最终的显著目标。该方法基于不同图像感知特征的特点,自适应提取显著目标,能够适应多变的检测目标与复杂的检测环境。实验结果表明,在受自然环境中背景干扰的情况下,该方法能有效检测出未知类别和不同尺度的显著目标。

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.