Region saliency detection via multi-feature on absorbing Markov chain

Saliency region detection plays an important role in image pre-processing, and uniformly emphasizing saliency region is still an intractable problem in computer vision. In this paper, we present a data-driven salient region detection method via multi-feature (included contrast, spatial relationship and background prior, etc.) on absorbing Markov chain, which uses super pixel to extract salient regions, and each super-pixel represents a node. In detail, we first construct function to calculate absorption probability of each node on absorbing Markov chain. Second we utilize image contrast and space relation to model the prior salient map which is provided to foreground salient nodes and then calculate the saliency of nodes based on absorption probability. Third, we also exploit background prior to supply the absorbing nodes and compute the saliency of nodes. Finally, we fuse both the saliency of nodes by cosine similarity measurement method and acquire the ultimate saliency map. Our approach is simple and efficient and highlights not only a single object but also multiple objects consistently. We test the proposed method on MSRA-B, iCoSeg and SED databases. Experimental results illustrate that the proposed approach presents better robustness and efficiency against the eleven state-of-the art algorithms.     

面向猴脸的吸收马尔科夫链图像显著性检测

传统的基于吸收马尔科夫链进行图像显著性检测方法只能检测出与图像背景差异较大的目标,或者位于图像中心的显著目标.但通常情况下,被关注的目标并不具有这样的条件.提出了一种面向对象的吸收马尔科夫链的显著性检测算法,并将其应用于金丝猴面部的显著性检测中.算法在传统的吸收马尔科夫链进行图像显著性检测的过程中,引入惩罚因子,依据一定的先验信息来动态调整吸收时间.根据超像素块与目标色彩信息之间的差异对颜色权重进行相应的奖励或惩罚,以指引算法能够正确提取多个显著目标.实验表明:相对于传统算法,算法能够更准确地检测出被关注的显著目标,尤其在图像中含有多个关注目标时,效果更加显著.     

背景吸收的马尔可夫显著性目标检测

目的 现有的基于马尔可夫链的显著目标检测方法是使用简单线性迭代聚类（SLIC）分割获得超像素块构造图的节点,再将四边界进行复制获得吸收节点,但是SLIC分割结果的好坏直接影响到后续结果,另外,很大一部分图像的显著目标会占据12个边界,特别是对于人像、雕塑等,如果直接使用四边界作为待复制的节点,必然影响最终效果。针对以上存在的缺陷,本文提出一种背景吸收的马尔可夫显著目标检测方法。方法 首先通过差异化筛选去除差异较大的边界,选择剩余3条边界上的节点进行复制作为马尔可夫链的吸收节点,通过计算转移节点的吸收时间获得初始的显著图,从初始显著图中选择背景可能性较大的节点进行复制作为吸收节点,再进行一次重吸收计算获得显著图,并对多层显著图进行融合获得最终的显著图。 结果 在ASD、DUT-OMRON和SED 3个公开数据库上,对比实验验证本文方法,与目前12种主流算法相比,在PR曲线、F值和直观上均有明显的提高,3个数据库计算出的F值分别为0.903、0.544 7、0.775 6,验证了算法的有效性。结论 本文针对使用图像边界的超像素块复制作为吸收节点和SLIC分割技术的缺陷,提出了一种基于背景吸收马尔可夫显著目标检测模型,实验表明,本文的方法适用于自底向上的图像显著目标检测,特别是适用于存在人像或雕塑等目标的图像,并且可以应用于图像检索、目标识别、图像分割和图像压缩等多个领域。     

Semantic feature based multi-spectral saliency detection

Multimedia Tools and Applications - Saliency detection aims to locate the distinctive regions in images and can be extensively applied to many applications. Up to now, most of effort has put into...     

Boosting color saliency in image feature detection

The aim of salient feature detection is to find distinctive local events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape-saliency of the local neighborhood. The majority of these detectors are luminance-based, which has the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. In this paper, color distinctiveness is explicitly incorporated into the design of saliency detection. The algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features.     

RGB-T salient object detection via CNN feature and result saliency map fusion

Applied Intelligence - Thermal infrared sensors have unique advantages under the conditions of insufficient illumination, complex scenarios, or occluded appearances. RGB-T salient object detection...     

Contextual-based top-down saliency feature weighting for target detection

Target detection using attention models has recently become a major research topic in active vision. One of the major problems in this area of research is how to appropriately weight low-level features to get high quality top-down saliency maps that highlight target objects. Learning of such weights has previously been done using example images having similar feature distributions without considering contextual information. In this paper, we propose a model that we refer to as the top-down contextual weighting (TDCoW) that incorporates high-level knowledge of the gist context of images to apply appropriate weights to the features. The proposed model is tested on four challenging datasets, two for cricket balls, one for bikes and one for person detection. The obtained results show the effectiveness of contextual information for modelling the TD saliency by producing better feature weights than those produced without contextual information.     

Salient object detection via multiple saliency weights

Multimedia Tools and Applications - Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects...     

基于HSV空间改进的多尺度显著性检测

图像显著性特征已被广泛地应用于图像分割、图像检索和图像压缩等领域,针对传统算法耗时较长,易受噪声影响等问题,提出了一种基于HSV色彩空间改进的多尺度显著性检测方法。该方法选择HSV色彩空间的色调、饱和度和亮度作为视觉特征,先通过高斯金字塔分解获得三种尺度的图像序列,然后使用改进的SR算法从三种尺度的图像序列中提出每个特征图,最后将这些特征图进行点对点的平方融合和线性融合。与其它算法的对比实验表明,该方法具有较好的检测效果和鲁棒性,能够较快速地检测出图像的显著性区域,能够突显整个显著性目标。     

Edge detection in the feature space

To build a consistent image representation model which can process the non-Gaussian distribution data, a novel edge detection method (KPCA-SCF) based on the kernel method is proposed. KPCA-SCF combines kernel principal component analysis and kernel subspace classification proposed in this paper to extract edge features. KPCA-SCF was tested and compared with linear PCA, nonlinear PCA and conventional methods such as Sobel, LOG, Canny, etc. Experiments on synthetic and real-world images show that KPCA-SCF is more robust under noisy conditions. KPCA-SCF's score of F-measure (0.44) ranks 11th in the Berkeley segmentation dataset and benchmark, it (0.54) ranks 10th tested on a noised image.     

Region-based depth feature descriptor for saliency detection on light field

Multimedia Tools and Applications - This paper addresses the light field saliency detection problem via a multiple cue integration framework. By reinterpreting the usage of dark channels in...     

基于改进LBE特征的RGB-D显著性检测

针对LBE算法难以完整检测出结构复杂的目标和过度依赖深度信息的问题,提出一种基于改进LBE特征的RGB-D显著性检测算法。首先,对输入图像进行多级分割;然后,在各级分割图上计算LBE特征并融合,得到深度显著图;最后,利用色彩信息和先验信息对深度显著图进行矫正得到最终显著图。实验结果表明,改进算法与原始LBE算法相比在准确率上略有降低,在召回率上明显提升,得到的显著图更接近真实值。     

Salient object detection via saliency bias and diffusion

Salient object detection aims to identify both spatial locations and scales of the salient object in an image. However, previous saliency detection methods generally fail in detecting the whole objects, especially when the salient objects are actually composed of heterogeneous parts. In this work, we propose a saliency bias and diffusion method to effectively detect the complete spatial support of salient objects. We first introduce a novel saliency-aware feature to bias the objectness detection for saliency detection on a given image and incorporate the saliency clues explicitly in refining the saliency map. Then, we propose a saliency diffusion method to fuse the saliency confidences of different parts from the same object for discovering the whole salient object, which uses the learned visual similarities among object regions to propagate the saliency values across them. Benefiting from such bias and diffusion strategy, the performance of salient object detection is significantly improved, as shown in the comprehensive experimental evaluations on four benchmark data sets, including MSRA-1000, SOD, SED, and THUS-10000.     

Network intrusion detection in covariance feature space

Detecting multiple and various network intrusions is essential to maintain the reliability of network services. The problem of network intrusion detection can be regarded as a pattern recognition problem. Traditional detection approaches neglect the correlation information contained in groups of network traffic samples which leads to their failure to improve the detection effectiveness. This paper directly utilizes the covariance matrices of sequential samples to detect multiple network attacks. It constructs a covariance feature space where the correlation differences among sequential samples are evaluated. Two statistical supervised learning approaches are compared: a proposed threshold based detection approach and a traditional decision tree approach. Experimental results show that both achieve high performance in distinguishing multiple known attacks while the threshold based detection approach offers an advantage of identifying unknown attacks. It is also pointed out that utilizing statistical information in groups of samples, especially utilizing the covariance information, will benefit the detection effectiveness.     

融合双特征图信息的图像显著性检测方法

目的 图像的显著性检测是将图像中最重要的、包含丰富信息的区域标记出来,并应用到图像分割、图像压缩、图像检索、目标识别等重要领域。针对现有研究方法显著性目标检测结果不完整以及单一依靠颜色差异检测方法的局限性,提出一种综合图像底层颜色对比特征图和图像颜色空间分布特征图的显著性检测方法,能够有效而完整地检测出图像中的显著性区域。方法 本文方法结合了SLIC超像素分割和K-means聚类算法进行图像特征的提取。首先,对图像进行SLIC（simple linear iterative clustering）分割,根据像素块之间的颜色差异求取颜色对比特征图;其次,按照颜色特征对图像进行K-means聚类,依据空间分布紧凑性和颜色分布统一性计算每个类的初步颜色空间分布特征。由于聚类结果中不包含空间信息,本文将聚类后的结果映射到超像素分割的像素块上,进一步优化颜色空间分布图;最后,通过融合颜色对比显著图和图像颜色空间分布特征图得到最终的显著图。结果 针对公开的图像测试数据库MSRA-1000,本文方法与当前几种流行的显著性检测算法进行了对比实验,实验结果表明,本文方法得到的显著性区域更准确、更完整。结论 本文提出了一种简单有效的显著性检测方法,结合颜色对比特征图和图像颜色空间分布特征图可以准确的检测出显著性区域。该结果可用于目标检测等实际问题,但该方法存在一定的不足,对于背景色彩过于丰富且与特征区域有近似颜色的图像,该方法得到的结果有待改进。今后对此算法的优化更加侧重于通用性。     

新的基于图像显著性区域特征的织物疵点检测算法

鉴于织物疵点类型的多样性和传统人工检测方法的低效率,为更有效地检测织物疵点,提出一种新的基于图像显著性特征的织物疵点检测方法——SGE。将原织物图分成相同两份:一份利用改进的基于频率的显著性区域(FSR)方法提取区域特征,粗定位疵点位置。另一份先Gabor滤波,取Gabor模图为输出特征;再利用基于像素的显著性区域(PSR)方法进行区域特征提取,细定位疵点位置;然后利用最大熵分别对粗细定位的疵点图进行分割,再融合;最后描绘轮廓,计算周长和面积,去除孤立点,得最终检测结果。采用OpenCV算法库,选取了4种具有代表的织物疵点图片进行验证。实验结果表明,这种粗细定位疵点的方法能够获得较好的检测结果,无需事先学习,能够满足实时性要求。     

Accurate saliency detection based on depth feature of 3D images

This paper presents an accurate saliency detection algorithm customized for 3D images which contain abundant depth cue. Firstly, depth feature is calculated based on the sharp regions’ positions within the focal stack. Then, we compute the coarse saliency map by subtracting the background region from the all-focus image according to the depth feature. Finally, we employ the contrast information in the coarse saliency map to obtain the final result. Experiments on light field dataset demonstrate that our approach favorably outperforms five state-of-the-art methods in terms of precision, recall and F-Measure. Moreover, the depth feature is validated to be a valuable complement to existing visual saliency analysis under the circumstance that the background regions are complex or similar to salient object regions.     

超像素条件随机场下的RGB-D视频显著性检测

目的 视觉显著性在众多视觉驱动的应用中具有重要作用,这些应用领域出现了从2维视觉到3维视觉的转换,从而基于RGB-D数据的显著性模型引起了广泛关注。与2维图像的显著性不同,RGB-D显著性包含了许多不同模态的线索。多模态线索之间存在互补和竞争关系,如何有效地利用和融合这些线索仍是一个挑战。传统的融合模型很难充分利用多模态线索之间的优势,因此研究了RGB-D显著性形成过程中多模态线索融合的问题。方法 提出了一种基于超像素下条件随机场的RGB-D显著性检测模型。提取不同模态的显著性线索,包括平面线索、深度线索和运动线索等。以超像素为单位建立条件随机场模型,联合多模态线索的影响和图像邻域显著值平滑约束,设计了一个全局能量函数作为模型的优化目标,刻画了多模态线索之间的相互作用机制。其中,多模态线索在能量函数中的权重因子由卷积神经网络学习得到。结果 实验在两个公开的RGB-D视频显著性数据集上与6种显著性检测方法进行了比较,所提模型在所有相关数据集和评价指标上都优于当前最先进的模型。相比于第2高的指标,所提模型的AUC（area under curve）,sAUC（shuffled AUC）,SIM（similarity）,PCC（Pearson correlation coefficient）和NSS（normalized scanpath saliency）指标在IRCCyN数据集上分别提升了2.3%,2.3%,18.9%,21.6%和56.2%;在DML-iTrack-3D数据集上分别提升了2.0%,1.4%,29.1%,10.6%,23.3%。此外还进行了模型内部的比较,验证了所提融合方法优于其他传统融合方法。结论 本文提出的RGB-D显著性检测模型中的条件随机场和卷积神经网络充分利用了不同模态线索的优势,将它们有效融合,提升了显著性检测模型的性能,能在视觉驱动的应用领域发挥一定作用。     

基于改进显著图和局部特征匹配的copy-move窜改检测

检测整幅窜改图像的方法增加了许多非必要的计算量,为了降低计算复杂度和进一步提高检测精确率,提出了一种基于改进显著图和局部特征匹配的copy-move窜改检测方法。首先,结合图像梯度改进显著图,分离出包含图像高纹理信息的局部显著区域;其次,只对该局部区域采用SIFT(scale invariant feature transform)算法提取特征点;然后,对显著性小的图像采用密度聚类和二阶段匹配策略,对显著性大的图像采用超像素分割和显著块特征匹配的策略;最后,结合PSNR和形态学操作来定位窜改区域。在两个公开数据集上进行实验,该方法的平均检测时间小于10 s,平均检测精确率大于97%,均优于所对比的方法。实验结果表明,该方法能够大幅缩减检测时间、有效提高检测精确率,并且对几何变换和后处理操作也都具有较好的鲁棒性。