基于背景先验与中心先验的显著性目标检测北大核心CSCD

针对传统的图流行排序显著性目标检测算法存在先验信息单一,显著目标检测不完整的问题,提出一种新的基于背景先验与中心先验的显著性目标检测算法。首先将图像边界节点作为背景种子进行流行排序获得粗略的前景区域,将其再次流行排序得到初步显著图;然后利用Harris角点检测、聚类实现中心先验显著性检测,捕获中心显著信息;最后在初步显著图上融合图像中心显著性,得到最终显著图。本文对综合指标、精确率-召回率曲线、F-measure值以及平均绝对误差(mean absolute error,MAE)值进行实验评估,在公开数据集MSRA-10K和ECSSD上进行的实验结果表明:对比10种主流算法,本文算法在不同的评估指标上都具有较好的表现,且能准确地突出显著目标,提升背景抑制效果。     

基于SVM和背景模型的显著性目标检测算法

针对显著性目标多样性和不确定性,机器学习算法无法检测没有先验信息的图像问题,提出了一种基于图像边缘信息构建背景模型结合SVM分类算法的显著性目标检测算法.该方法对输入图像进行超像素预处理,使像素级转化为超像素级,既抑制噪声,又提高了计算效率.利用图像边缘超像素构建图像的初始背景模型,得到初始显著图.基于SVM算法建立目...     

融合高低层多特征的显著性检测算法

针对单一显著性特征无法全面表达图像显著性致使显著性检测精度不高等问题，本文提出了一种多特征融合的显著性检测算法。算法在高层先验知识基础上，对靠近中心的超像素设置高显著值，利用高斯分布求解中心先验；在底层特征上融合图像的边界稀疏、全局对比度、颜色空间分布和超级像素差异等4种显著特征，利用类间差异最大阈值对高低层特征进行线性和非线性融合，最终得到高质量的显著图。在MSRA-1000、SED、SOD 3个公开的数据集上进行实验，结果表明：本文算法融合得到的显著图边缘清晰、显著区域突出均匀，在有效抑制背景信息的同时所得显著图像视觉感知更好，与其他显著性算法相比查全率和查准率上至少提高3.4%。     

选择性背景优先的显著性检测模型

在检测图像显著性区域的领域中,背景优先是一个较新的思路,但会遇到背景鉴别这个具有挑战性的难题。该文提出背景真实性的判断问题,在探索的过程中发现背景通常具有连续性的特征,根据这一特性实现了判定背景的方法,并将判断的结果作为显著性先验值应用于后继的计算中,最终结果的准确性和正确性得到有效提高。该文首先采用均值漂移(MS)分割算法将图片预分为超像素,计算所有超像素的初始显著值;随后提取原图的4个边界条,计算每两条之间的色彩直方图距离,判定小于预设阈值的两条边界作为真的背景,选择它们作为优先边界,计算先验显著性值;最后进行显著性计算,得到最终的显著图。实验结果表明,该算法能够准确检测出显著性区域,与其他6种算法相比具有较大优势。     

超像素和阈值分割相结合的显著目标检测算法

现有的显著性检测结果普遍含噪及未能完整高亮显著物体,使得后续的显著目标检测仍是一个具有挑战性的问题。提出了一种结合超像素分割和阈值分割的新的显著目标检测算法。算法首先利用超像素分割方法对原图像进行分割计算,然后依据显著性检测结果计算每一个分割区域像素的平均显著度值,接着用平均显著度值表示超像素内每一个像素的原显著度值,最后根据阈值分割算法对其进行计算获取二值掩码图以表示显著目标检测结果。实验结果表明,在4种具有代表性的显著图上,所提算法能有效检测显著目标,具有较高的正确率、召回率和F度量值。     

基于超像素时空显著性的运动目标检测算法

针对复杂背景下运动目标检测存在的背景干扰、目标分割不完整等问题,利用目标静态灰度特征和运动特征,结合目标运动连续特性,提出了一种基于超像素时空显著图的运动目标检测算法。首先对图像基于简单线性迭代聚类算法(SLIC)进行超像素分割,以初始超像素为节点、以运动特征差异性为边建立图结构对超像素区域进行合并,得到最终超像素图像,可以有效解决传统超像素分割方法过分割而导致目标被分为多个部分的问题;然后分别利用目标静态特征对比度和运动特征对比度,得到静态显著性图和运动显著性图,并融合得到最终的时空显著性图;最后利用恒虚警处理技术,结合运动连续特性实现目标的检测,可以有效减少虚警目标。实验结果表明,该算法针对复杂背景具有良好的鲁棒性,并且可以比较完整的保留目标的信息。     

基于两重误差重构的显著性区域检测算法

通过SLIC分割算法将图像分成多个超像素区域后,利用重构误差进行视觉显著性检测。首先提取图像边缘的超像素区域作为背景模板,然后利用这些模板构建两重外观模型:稀疏外观模型及稠密外观模型。对于每一块图像区域,首先计算稠密重构误差及稀疏重构误差,然后利用K均值聚类方法得到的上下文对重构误差进行传播,再利用贝叶斯准则融合稀疏型检测结果及稠密型检测结果,最后通过综合多尺度重构误差信息及修正的目标基高斯模型信息实现像素级显著性检测。     

超复数域小波变换的显著性检测

针对现有频域显著性检测方法得到的显著区域不完整的问题,该文提出一种多尺度分析的频率域显著性检测方法。首先由输入图像特征通道信息构建4元超复数,然后通过小波变换对4元超复数域中幅度谱进行多尺度分解,计算生成多尺度下的视觉显著图,最后由评价函数选出效果较好显著图合成最终视觉显著图。实验结果表明,该文方法能够有效地抑制背景干扰,快速、精确地找到完整的显著目标,具有较高的检测精确度。     

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.     

基于优化的极限学习机和深度层次的RGB-D显著检测

目前,相当多的显著目标检测方法均聚焦于2D的图像上,而RGB-D图像所需要的显著检测方法与单纯的2D图像相去甚远,这就需要新的适用于RGB-D的显著检测方法。该文在经典的RGB显著检测方法,即极限学习机的应用的基础上,提出融合了特征提取、前景增强、深度层次检测等多种思路的新的RGB-D显著性检测方法。该文的方法是:第一,运用特征提取的方法,提取RGB图4个超像素尺度的4096维特征;第二,依据特征提取中产生的4个尺度的超像素数量,分别提取RGB图的RGB, LAB, LBP特征以及深度图的LBE特征;第三,根据LBE和暗通道特征两种特征求出粗显著图,并在4个尺度的遍历中不断强化前景、削弱背景;第四,根据粗显著图选取前景与背景种子,放入极限学习机中进行分类,得到第1阶段显著图;第五,运用深度层次检测、图割等方法对第1阶段显著图进行再次优化,得到第2阶段显著图,即最终显著图。     

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.     

Superpixel based color contrast and color distribution driven salient object detection

Color is the most informative low-level feature and might convey tremendous saliency information of a given image. Unfortunately, color feature is seldom fully exploited in the previous saliency models. Motivated by the three basic disciplines of a salient object which are respectively center distribution prior, high color contrast to surroundings and compact color distribution, in this paper, we design a comprehensive salient object detection system which takes the advantages of color contrast together with color distribution and outputs high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, and final refinement.In color contrast saliency computation, we calculate center-surrounded color contrast and then employ the distribution prior in order to select correct color components. A global saliency smoothing procedure that is based on superpixel regions is introduced as well. This processing step preferably alleviates the saliency distortion problem, leading to the entire object being highlighted uniformly. Finally, a saliency refinement approach is adopted to eliminate artifacts and recover unconnected parts within the combined saliency maps.In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutter. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets. Excellent content-aware image resizing also could be achieved using our saliency maps.     

High-level background prior based salient object detection

Salient object detection is a fundamental problem in computer vision. Existing methods using only low-level features failed to uniformly highlight the salient object regions. In order to combine high-level saliency priors and low-level appearance cues, we propose a novel Background Prior based Salient detection method (BPS) for high-quality salient object detection.Different from other background prior based methods, a background estimation is added before performing saliency detection. We utilize the distribution of bounding boxes generated by a generic object proposal method to obtain background information. Three background priors are mainly considered to model the saliency, namely background connectivity prior, background contrast prior and spatial distribution prior, allowing the proposed method to highlight the salient object as a whole and suppress background clutters.Experiments conducted on two benchmark datasets validate that our method outperforms 11 state-of-the-art methods, while being more efficient than most leading methods.     

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.     

Global salient information maximization for saliency detection

In this paper, a new method for saliency detection is proposed. Based on the defined features of the salient object, we solve the problem of saliency detection from three aspects. Firstly, from the view of global information, we partition the image into two clusters, namely, salient component and background component by employing Principal Component Analysis (PCA) and k-means clustering. Secondly, the maximal salient information is applied to find the position of saliency and eliminate the noise. Thirdly, we enhance the saliency for the salient regions while weaken the background regions. Finally, the saliency map is obtained based on these aspects. Experimental results show that the proposed method achieves better results than the state of the art methods. And this method can be applied for graph based salient object segmentation.     

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.     

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.