视觉注意驱动的基于混沌分析的运动检测方法

提出了视觉注意驱动的基于混沌分析的运动检测方法(MDSA)。MDSA首先基于视觉注意机制提取图像的显著区域,而后对显著区域进行混沌分析以检测运动目标。算法技术路线为:首先根据场景图像提取多种视觉敏感的底层图像特征;然后根据特征综合理论将这些特征融合起来得到一幅反映场景图像中各个位置视觉显著性的显著图;而后对显著性水平最高的图像位置所在的显著区域运用混沌分析的方法进行运动检测;根据邻近优先和返回抑制原则提取下一最显著区域并进行运动检测,直至遍历所有的显著区域。本文对传统的显著区域提取方法进行了改进以减少计算量:以邻域标准差代替center-surround算子评估图像各位置的局部显著度,采用显著点聚类的方法代替尺度显著性准则提取显著区域;混沌分析首先判断各显著区域的联合直方图（JH）是否呈现混沌特征,而后依据分维数以一固定阈值对存在混沌的JH中各散点进行分类,最后将分类结果对应到显著区域从而实现运动分割。MDSA具有较好的运动分割效果和抗噪性能,对比实验和算法开销分析证明MDSA优于基于马塞克的运动检测方法（MDM）。      

Image saliency detection based on geodesic‐like and boundary contrast maps

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high‐contrast background, but they have no effect on the extraction of a salient object from images with complex low‐contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics‐like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low‐contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low‐contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state‐of‐the‐art approaches, the proposed approach performs well.     

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.     

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.     

Exploiting object features in deep gaze prediction models

The human visual system analyzes the complex scenes rapidly. It devotes the limited perceptual resources to the most salient subsets and/or objects of scenes while ignoring their less salient parts. Gaze prediction models try to predict the human eye fixations (human gaze) under free-viewing conditions while imitating the attentive mechanism. Previous studies on saliency benchmark datasets have shown that visual attention is affected by the salient objects of the scenes and their features. These features include the identity, the location, and the visual features of objects in the scenes, beside to the context of the input image. Moreover, the human eye fixations often converge to the specific parts of salient objects in the scenes. In this paper, we propose a deep gaze prediction model using object detection via image segmentation. It uses some deep neural modules to find the identity, location, and visual features of the salient objects in the scenes. In addition, we introduce a deep module to capture the prior bias of human eye fixations. To evaluate our model, several challenging saliency benchmark datasets are used in the experiments. We also conduct an ablation study to show the effectiveness of our proposed modules and its architecture. Despite its fewer parameters, our model has comparable, or even better performance on some datasets, to the state-of-the-art saliency models.     

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。     

基于弱监督E2LSH和显著图加权的目标分类方法

在目标分类领域,当前主流的目标分类方法是基于视觉词典模型,而时间效率低、视觉单词同义性和歧义性及单词空间信息的缺失等问题严重制约了其分类性能。针对这些问题,该文提出一种基于弱监督的精确位置敏感哈希(E2LSH)和显著图加权的目标分类方法。首先,引入E2LSH算法对训练图像集的特征点聚类生成一组视觉词典,并提出一种弱监督策略对E2LSH中哈希函数的选取进行监督,以降低其随机性,提高视觉词典的区分性。然后,利用GBVS(Graph-Based Visual Saliency)显著度检测算法对图像进行显著度检测,并依据单词所处区域的显著度值为其分配权重;最后,利用显著图加权的视觉语言模型完成目标分类。在数据集Caltech-256和Pascal VOC 2007上的实验结果表明,所提方法能够较好地提高词典生成效率,提高目标表达的分辨能力,其目标分类性能优于当前主流方法。     

基于带汇点Laplace扩散模型的显著目标检测

该文基于Laplace相似度量的构造方法,针对两阶段显著目标检测中显著种子的不同类型(稀疏或稠密),提出了相应的显著性扩散模型,从而实现了基于扩散的两阶段互补的显著目标检测。尤其是第2阶段扩散模型中汇点的融入,一方面更好地抑制了显著性图中的背景,同时对于控制因子的取值更加稳健。实验结果表明,当显著种子确定时,不同的扩散模型会导致显著性扩散程度的差异。基于带汇点Laplace的两阶段互补的扩散模型较其他扩散模型更有效、更稳健。同时,从多项评价指标分析,该算法与目前流行的5种显著目标检测算法相比,具有较大优势。这表明此种用于图像检索或分类的Laplace相似度量的构造方法在显著目标检测中也是适用的。     

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.     

基于鲁棒视觉变换和多注意力的全景图像显著性检测

针对当前全景图像显著性检测方法存在检测精度偏低、模型收敛速度慢和计算量大等问题,该文提出一种基于鲁棒视觉变换和多注意力的U型网络(URMNet)模型。该模型使用球形卷积提取全景图像的多尺度特征,减轻了全景图像经等矩形投影后的失真。使用鲁棒视觉变换模块提取4种尺度特征图所包含的显著信息,采用卷积嵌入的方式降低特征图的分辨率,增强模型的鲁棒性。使用多注意力模块,根据空间注意力与通道注意力间的关系,有选择地融合多维度注意力。最后逐步融合多层特征,形成全景图像显著图。纬度加权损失函数使该文模型具有更快的收敛速度。在两个公开数据集上的实验表明,该文所提模型因使用了鲁棒视觉变换模块和多注意力模块,其性能优于其他6种先进方法,能进一步提高全景图像显著性检测精度。     

AFLNet: Adversarial focal loss network for RGB-D salient object detection

Because salient objects usually have fewer data in a scene, the problem of class imbalance is often encountered in salient object detection (SOD). In order to address this issue and achieve the consistent salient objects, we propose an adversarial focal loss network with improving generative adversarial networks for RGB-D SOD (called AFLNet), in which color and depth branches constitute the generator to achieve the saliency map, and adversarial branch with high-order potentials, instead of pixel-wise loss function, refines the output of the generator to obtain contextual information of objects. We infer the adversarial focal loss function to solve the problem of foreground–background class imbalance. To sufficiently fuse the high-level features of color and depth cues, an inception model is adopted in deep layers. We conduct a large number of experiments using our proposed model and its variants, and compare them with state-of-the-art methods. Quantitative and qualitative experimental results exhibit that our proposed approach can improve the accuracy of salient object detection and achieve the consistent objects.     

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。     

Salient region detection through sparse reconstruction and graph-based ranking

In this paper, we propose a salient region detection algorithm from the point of view of unique and compact representation of individual image. In first step, the original image is segmented into super-pixels. In second step, the sparse representation measure and uniqueness of the features are computed. Then both are ranked on the basis of the background and foreground seeds respectively. Thirdly, a location prior map is used to enhance the foci of attention. We apply the Bayes procedure to integrate computed results to produce smooth and precise saliency map. We compare our proposed algorithm against the state-of-the-art saliency detection methods using four of the largest widely available standard data-bases, experimental results specify that the proposed algorithm outperforms. We also show that how the saliency map of the proposed method is used to discover outline of object, furthermore using this outline our method produce the saliency cut of the desired object.     

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。     

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.     

基于结构标签学习的显著性目标检测

提出了一种基于结构标签学习的显著性目标检测算法,将结构化学习方法应用到显著性目标检测中。首先从含有标记的图像中随机采集固定大小的矩形区域,并记录其结构标签;然后使用含结构标签的区域特征构建决策树集合;最后采用监督学习的方法对图像进行优化预测,得到最终的显著图。实验结果表明,本文方法能较准确地检测出图像库中图像的显著性区域,在数据库MSRA5000和BSD300的AUC值分别为0.891 8、0.705 2,说明本文方法具有较好的显著性检测效果。     

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。     

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.