Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applicatio...Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.展开更多
显著性目标检测是获取图像中视觉显著目标的任务,它是计算机视觉及相关研究领域的重要内容。当前在复杂的自然场景下基于深度学习的算法依然存在特征学习不足和检测错误率较高的问题,因此提出一种新颖的基于多特征融合的显著性目标检测...显著性目标检测是获取图像中视觉显著目标的任务,它是计算机视觉及相关研究领域的重要内容。当前在复杂的自然场景下基于深度学习的算法依然存在特征学习不足和检测错误率较高的问题,因此提出一种新颖的基于多特征融合的显著性目标检测算法。以HDHF(hybrid deep and handcrafted feature)模型的预测显著图作为特征,融合全局像素的深度特征。此外,利用显著性提名获取候选目标的位置,并在各候选目标中添加中心先验。在全卷积神经网络中,利用前向传播算法最终预测得到像素级的显著性目标。在四个包含多个显著性目标和复杂背景的图像数据集上进行验证,实验结果表明,该算法有效地提高了复杂场景下显著性目标的检测精度,尤其是在背景复杂的图像上具有较优的检测效果。展开更多
alient object detection aims at identifying the visually interesting object regions that are consistent with human perception. Multispectral remote sensing images provide rich radiometric information in revealing the ...alient object detection aims at identifying the visually interesting object regions that are consistent with human perception. Multispectral remote sensing images provide rich radiometric information in revealing the physical properties of the observed objects, which leads to great potential to perform salient object detection for remote sensing images. Conventional salient object detection methods often employ handcrafted features to predict saliency by evaluating the pixel-wise or superpixel-wise contrast. With the recent use of deep learning framework, in particular, fully convolutional neural networks, there has been profound progress in visual saliency detection. However, this success has not been extended to multispectral remote sensing images, and existing multispectral salient object detection methods are still mainly based on handcrafted features, essentially due to the difficulties in image acquisition and labeling. In this paper, we propose a novel deep residual network based on a top-down model, which is trained in an end-to-end manner to tackle the above issues in multispectral salient object detection. Our model effectively exploits the saliency cues at different levels of the deep residual network. To overcome the limited availability of remote sensing images in training of our deep residual network, we also introduce a new spectral image reconstruction model that can generate multispectral images from RGB images. Our extensive experimental results using both multispectral and RGB salient object detection datasets demonstrate a significant performance improvement of more than 10% improvement compared with the state-of-the-art methods.展开更多
文摘Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.
文摘显著性目标检测是获取图像中视觉显著目标的任务,它是计算机视觉及相关研究领域的重要内容。当前在复杂的自然场景下基于深度学习的算法依然存在特征学习不足和检测错误率较高的问题,因此提出一种新颖的基于多特征融合的显著性目标检测算法。以HDHF(hybrid deep and handcrafted feature)模型的预测显著图作为特征,融合全局像素的深度特征。此外,利用显著性提名获取候选目标的位置,并在各候选目标中添加中心先验。在全卷积神经网络中,利用前向传播算法最终预测得到像素级的显著性目标。在四个包含多个显著性目标和复杂背景的图像数据集上进行验证,实验结果表明,该算法有效地提高了复杂场景下显著性目标的检测精度,尤其是在背景复杂的图像上具有较优的检测效果。
基金National 1000 Young Talents Plan of ChinaNational Natural Science Foundation of China(61420106007,61671387,61871325)DECRA of Australica Resenrch Council (DE140100180).
文摘alient object detection aims at identifying the visually interesting object regions that are consistent with human perception. Multispectral remote sensing images provide rich radiometric information in revealing the physical properties of the observed objects, which leads to great potential to perform salient object detection for remote sensing images. Conventional salient object detection methods often employ handcrafted features to predict saliency by evaluating the pixel-wise or superpixel-wise contrast. With the recent use of deep learning framework, in particular, fully convolutional neural networks, there has been profound progress in visual saliency detection. However, this success has not been extended to multispectral remote sensing images, and existing multispectral salient object detection methods are still mainly based on handcrafted features, essentially due to the difficulties in image acquisition and labeling. In this paper, we propose a novel deep residual network based on a top-down model, which is trained in an end-to-end manner to tackle the above issues in multispectral salient object detection. Our model effectively exploits the saliency cues at different levels of the deep residual network. To overcome the limited availability of remote sensing images in training of our deep residual network, we also introduce a new spectral image reconstruction model that can generate multispectral images from RGB images. Our extensive experimental results using both multispectral and RGB salient object detection datasets demonstrate a significant performance improvement of more than 10% improvement compared with the state-of-the-art methods.
