基于深度学习的图像显著对象检测

显著区域检测可应用在对象识别、图像分割、视 频/图像压缩中,是计算机视觉领域的重要研究主题。然而,基于不 同视觉显著特征的显著区域检测法常常不能准确地探测出显著对象且计算费时。近来,卷积 神经网络模型在图像分析和处理 领域取得了极大成功。为提高图像显著区域检测性能,本文提出了一种基于监督式生成对抗 网络的图像显著性检测方法。它 利用深度卷积神经网络构建监督式生成对抗网络,经生成器网络与鉴别器网络的不断相互对 抗训练,使卷积网络准确学习到 图像显著区域的特征,进而使生成器输出精确的显著对象分布图。同时,本文将网络自身误 差和生成器输出与真值图间的 L1距离相结合,来定义监督式生成对抗网络的损失函数,提升了显著区域检测精度。在MSRA 10K与ECSSD数据库上的实 验结果表明,本文方法 分别获得了94.19%与96.24%的准确率和93.99%与90.13%的召回率,F -Measure值也高达94.15%与94.76%,优于先 前常用的显著性检测模型。     

Two-stage salient region detection by exploiting multiple priors

Saliency detection has become a valuable tool for many image processing tasks, like image retargeting, object recognition, and adaptive compression. With the rapid development of the saliency detection methods, people have approved the hypothesis that “the appearance contrast between the salient object and the background is high”, and build their saliency methods on some priors that explain this hypothesis. However, these methods are not satisfactory enough. We propose a two-stage salient region detection method. The input image is first segmented into superpixels. In the first stage, two measures which measure the isolation and distribution of each superpixel are proposed, we consider that both of these two measures are important for finding the salient regions, thus the image-feature-based saliency map is obtained by combining the two measures. Then, in the second stage, we incorporate into the image-feature-based saliency map a location prior map to emphasize the foci of attention. In this algorithm, six priors that explain what is the salient region are exploited. The proposed method is compared with the state-of-the-art saliency detection methods using one of the largest publicly available standard databases, the experimental result indicates that the proposed method has better performance. We also demonstrate how the saliency map of the proposed method can be used to create high quality of initial segmentation masks for subsequent image processing, like Grabcut based salient object segmentation.     

基于多特征扩散方法的显著性物体检测

现有的大部分基于扩散理论的显著性物体检测方法只用了图像的底层特征来构造图和扩散矩阵,并且忽视了显著性物体在图像边缘的可能性。针对此,该文提出一种基于图像的多层特征的扩散方法进行显著性物体检测。首先,采用由背景先验、颜色先验、位置先验组成的高层先验方法选取种子节点。其次,将选取的种子节点的显著性信息通过由图像的底层特征构建的扩散矩阵传播到每个节点得到初始显著图,并将其作为图像的中层特征。然后结合图像的高层特征分别构建扩散矩阵,再次运用扩散方法分别获得中层显著图、高层显著图。最后,非线性融合中层显著图和高层显著图得到最终显著图。该算法在3个数据集MSRA10K,DUT-OMRON和ECSSD上,用3种量化评价指标与现有4种流行算法进行实验结果对比,均取得最好的效果。     

基于区域特征融合的RGBD显著目标检测

为了对各类自然场景中的显著目标进行检测,本文提出了一种将图像的深度信息引入区域显著性计算的方法,用于目标检测。首先对图像进行多尺度分割得到若干区域,然后对区域多类特征学习构建回归随机森林,采用监督学习的方法赋予每个区域特征显著值,最后采用最小二乘法对多尺度的显著值融合,得到最终的显著图。实验结果表明,本文算法能较准确地定位RGBD图像库中每幅图的显著目标。     

Bayesian salient object detection based on saliency driven clustering

Salient object detection is essential for applications, such as image classification, object recognition and image retrieval. In this paper, we design a new approach to detect salient objects from an image by describing what does salient objects and backgrounds look like using statistic of the image. First, we introduce a saliency driven clustering method to reveal distinct visual patterns of images by generating image clusters. The Gaussian Mixture Model (GMM) is applied to represent the statistic of each cluster, which is used to compute the color spatial distribution. Second, three kinds of regional saliency measures, i.e, regional color contrast saliency, regional boundary prior saliency and regional color spatial distribution, are computed and combined. Then, a region selection strategy integrating color contrast prior, boundary prior and visual patterns information of images is presented. The pixels of an image are divided into either potential salient region or background region adaptively based on the combined regional saliency measures. Finally, a Bayesian framework is employed to compute the saliency value for each pixel taking the regional saliency values as priority. Our approach has been extensively evaluated on two popular image databases. Experimental results show that our approach can achieve considerable performance improvement in terms of commonly adopted performance measures in salient object detection.     

Saliency detection using joint spatial-color constraint and multi-scale segmentation

In this paper, a novel method is proposed to detect salient regions in images. To measure pixel-level saliency, joint spatial-color constraint is defined, i.e., spatial constraint (SC), color double-opponent (CD) constraint and similarity distribution (SD) constraint. The SC constraint is designed to produce global contrast with ability to distinguish the difference between “center and surround”. The CD constraint is introduced to extract intensive contrast of red-green and blue-yellow double opponency. The SD constraint is developed to detect the salient object and its background. A two-layer structure is adopted to merge the SC, CD and SD saliency into a saliency map. In order to obtain a consistent saliency map, the region-based saliency detection is performed by incorporating a multi-scale segmentation technique. The proposed method is evaluated on two image datasets. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection as well as human fixation prediction.     

基于L0平滑的超像素块最短测地距离的显著区域提取方法

为了有效地检测出图像中的显著区域,提出了一种基于L₀平滑 的超像素块最短测地距离的图像显著区域提取方法。首先对图像进行L₀平滑得到边缘锐化内部细节平滑 的图像,进而用简单线性迭代聚类(SLIC)算法得到图像的超像素块;然后提取超像素块的 颜色和空间特征,计 算超像素块之间的最短测地距离,根据图像边界的连通性得到每个超像素块的初步显著值; 最后通过显著 区域聚焦等后处理优化操作,滤除图中的背景噪声,得到最终显著图。利用公开数据库与之 前方法进行 比较的结果显示,本文方法能够更好地加亮整个显著目标区域,去除背景干扰区域。通过 ROC面积的比较,证明了本文提出方法的有效性。     

超像素内容感知先验的多尺度贝叶斯显著性检测方法

针对复杂背景下显著性检测方法不能够有效地抑制背景,进而准确地检测目标这一问题,提出了超像素内容感知先验的多尺度贝叶斯显著性检测方法.首先,将目标图像分割为多尺度的超像素图,在每个尺度上引入内容感知的对比度先验、中心位置先验、边界连通背景先验来计算单一尺度上的目标显著值;其次,融合多个尺度的内容感知先验显著值生成一个粗略的显著图;然后,将粗略显著图值作为先验概率,根据颜色直方图和凸包中心先验计算观测似然概率,再使用多尺度贝叶斯模型来获取最终显著目标;最后,使用了3个公开的数据集、5种评估指标、7种现有的方法进行对比实验,结果表明本文方法在显著性目标检测方面具有更好的表现.     

Salient object detection via a boundary-guided graph structure

Graph-based salient object detection methods have gained more and more attention recently. However, existing works fail to separate effectively salient object and background in some challenging scenes. Inspired by this observation, we propose an effective salient object detection method based on a novel boundary-guided graph structure. More specifically, the input image is firstly segmented into a series of superpixels. Then we integrate two prior cues to generate the coarse saliency map, a novel weighting mechanism is proposed to balance the proportion of two prior cues according to their performance. Secondly, we propose a novel boundary-guided graph structure to explore deeply the intrinsic relevance between superpixels. Based on the proposed graph structure, an iterative propagation mechanism is constructed to refine the coarse saliency map. Experimental results on four datasets show adequately the superiority of the proposed method than other state-of-the-art methods.     

Salient object detection via boosting object-level distinctiveness and saliency refinement

Many salient object detection approaches share the common drawback that they cannot uniformly highlight heterogeneous regions of salient objects, and thus, parts of the salient objects are not discriminated from background regions in a saliency map. In this paper, we focus on this drawback and accordingly propose a novel algorithm that more uniformly highlights the entire salient object as compared to many approaches. Our method consists of two stages: boosting the object-level distinctiveness and saliency refinement. In the first stage, a coarse object-level saliency map is generated based on boosting the distinctiveness of the object proposals in the test images, using a set of object-level features and the Modest AdaBoost algorithm. In the second stage, several saliency refinement steps are executed to obtain a final saliency map in which the boundaries of salient objects are preserved. Quantitative and qualitative comparisons with state-of-the-art approaches demonstrate the superior performance of our approach.     

基于流形排名与迟滞阈值法的视觉显著性检测

视觉显著性检测是机器视觉领域的关键技术之一.提出一种基于流形排名与迟滞阈值的检测方法,首先将图像划分成超像素集合,以之作为结点形成闭环图;再按照基于图的流形排名方法计算各个结点的显著值,形成图像的显著图;然后利用显著图直方图统计出高、低两个阈值,将显著图划分为三个部分,使用伽马校正技术分别进行处理,最终整合校正结果得到输出显著图.实验结果表明,相对于现有算法,本文算法得到的显著图能够更好地区分背景区域和显著目标,同时也更具稳健性.     

融合显著图和保真图的全参考图像质量评价

提出一种融合显著图(SM)和保真图(FM)的全参考图 像质量 评价算法,用于评价质降图像的失真度。利用亮度和色度的相似度提取质降图像相对于 参考图像的FM;对参考图像进行区域划分、全局显著性提取和纹理边缘补充得到SM,将SM与 质降图像的FM融合得到基 于感知的显著保真图(PSM),计算质降图像的客观评价得分。在标准数据库上的实验结果表 明,本文方法与主观评价能够很好保持一致,并对LIVE图像库中的5种失真图像均有很好的 表现。     

基于对比敏感度的小波域图像显著性检测

A method of high definition saliency detection based on contrast sensitive function and wavelet analysis was proposed in order to improve the resolution of saliency maps.Original image was filtered by contrast sensitive function in YCbCr space,which could simulate the contrast of human eyes; then wavelet decomposition was carried out in Y,Cb,and Cr three channels individually,low frequency and high frequency feature saliency maps were extracted and further combined to obtain saliency map in single channel; finally saliency maps in three channels were fused to the high resolution saliency map.Experiments result show that the saliency images have high resolution,well-defined boundaries,and whole highlight salient objects.     

Salient object detection in video using deep non-local neural networks

Detection of salient objects in image and video is of great importance in many computer vision applications. In spite of the fact that the state of the art in saliency detection for still images has been changed substantially over the last few years, there have been few improvements in video saliency detection. This paper proposes a novel non-local fully convolutional network architecture for capturing global dependencies more efficiently and investigates the use of recently introduced non-local neural networks in video salient object detection. The effect of non-local operations is studied separately on static and dynamic saliency detection in order to exploit both appearance and motion features. A novel deep non-local fully convolutional network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods.     

Surroundedness based multiscale saliency detection

In this paper, a surroundedness-based multiscale saliency method is proposed based on the Gestalt principles for figure-ground segregation, which states that (1) surrounded regions are more likely to be perceived as figures, (2) the humans understand the external stimuli as whole rather than the sum of their parts. First, an image is characterized by a set of binary images, which is generated by a simple and effective homogeneous region extraction method with well contour preservation. And the contour confidence map is obtained by a fast contour detection method. Then for each connect homogeneous region in a binary map, surroundedness is defined by the average outer contour confidence. Finally, integrating the background priors, multiscale saliency maps are generated and combined to the final saliency map. The proposed method is evaluated on two widely used public datasets with pixel accurate salient region annotations using both precision and recall analysis and ROC analysis. And the experimental results show that the proposed method outperforms 14 alternative methods.     

Saliency detection based on singular value decomposition

Saliency detection has gained popularity in many applications, and many different approaches have been proposed. In this paper, we propose a new approach based on singular value decomposition (SVD) for saliency detection. Our algorithm considers both the human-perception mechanism and the relationship between the singular values of an image decomposed by SVD and its salient regions. The key concept of our proposed algorithms is based on the fact that salient regions are the important parts of an image. The singular values of an image are divided into three groups: large, intermediate, and small singular values. We propose the hypotheses that the large singular values mainly contain information about the non-salient background and slight information about the salient regions, while the intermediate singular values contain most or even all of the saliency information. The small singular values contain little or even none of the saliency information. These hypotheses are validated by experiments. By regularization based on the average information, regularization using the leading largest singular values or regularization based on machine learning, the salient regions will become more conspicuous. In our proposed approach, learning-based methods are proposed to improve the accuracy of detecting salient regions in images. Gaussian filters are also employed to enhance the saliency information. Experimental results prove that our methods based on SVD achieve superior performance compared to other state-of-the-art methods for human-eye fixations, as well as salient-object detection, in terms of the area under the receiver operating characteristic (ROC) curve (AUC) score, the linear correlation coefficient (CC) score, the normalized scan-path saliency (NSS) score, the F-measure score, and visual quality.     

基于加权的K近邻线性混合显著性目标检测

显著性目标检测旨在于一个场景中自动检测能够引起人类注意的目标或区域,在自底向上的方法中,基于多核支持向量机(SVM)的集成学习取得了卓越的效果。然而,针对每一张要处理的图像,该方法都要重新训练,每一次训练都非常耗时。因此,该文提出一个基于加权的K近邻线性混合(WKNNLB)显著性目标检测方法:利用现有的方法来产生初始的弱显著图并获得训练样本,引入加权的K近邻(WKNN)模型来预测样本的显著性值,该模型不需要任何训练过程,仅需选择一个最优的K值和计算与测试样本最近的K个训练样本的欧式距离。为了减少选择K值带来的影响,多个加权的K近邻模型通过线性混合的方式融合来产生强的显著图。最后,将多尺度的弱显著图和强显著图融合来进一步提高检测效果。在常用的ASD和复杂的DUT-OMRON数据集上的实验结果表明了该算法在运行时间和性能上的有效性和优越性。当采用较好的弱显著图时,该算法能够取得更好的效果。     

基于可区分边界和加权对比度优化的显著度检测算法

针对目前基于先验背景的显著度算法中,把图像的所有边界同等对待带来的误判别问题,本文提出一种基于可区分边界和加权对比度优化的显著度检测算法.为了客观评价显著度,本文首先设计了一种粗略评估显著度的指标,用来选择较好的背景图.以该指标为基础,该算法先利用Hausdorff距离对边界进行区分,再利用测地线距离变换完成可靠的背景检测;然后,构造了一种前景-背景加权的对比度来计算初始显著度;最后,使用加权的优化模型进行显著度的优化.在5个公开数据集上的实验结果表明,本文算法在保持快速、无训练等优点的同时,检测性能优于目前主流算法.     

Salient object detection using local,global and high contrast graphs

In this paper, we propose a novel multi-graph-based method for salient object detection in natural images. Starting from image decomposition via a superpixel generation algorithm, we utilize color, spatial and background label to calculate edge weight matrix of the graphs. By considering superpixels as the nodes and region similarities as the edge weights, local, global and high contrast graphs are created. Then, an integration technique is applied to form the saliency maps using degree vectors of the graphs. Extensive experiments on three challenging datasets show that the proposed unsupervised method outperforms the several different state-of-the-art unsupervised methods.