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.     

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...     

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...     

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.     

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.     

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.     

基于图像显著区域检测的SIFT特征匹配方法研究

针对目标检测中利用SIFT算法在提取图像特征时提取的背景特征点所占比例较大,提出了一种图像显著区域与SIFT算法相结合的目标匹配方法。为使检测出的极值点与人眼观察到的极值点相似,提出对尺度空间中的图像进行显著区域的检测;为了使特征点具有仿射不变性,对特征点进行椭圆拟合;特征匹配时引入夹角余弦相似度测度方法。实验表明,该算法在实时性以及匹配准确率方面都优于传统的SIFT算法。     

Quantization-based Markov feature extraction method for image splicing detection

In this paper, we propose an efficient Markov feature extraction method for image splicing detection using discrete cosine transform coefficient quantization. The quantization operation reduces the information loss caused by the coefficient thresholding used to restrict the number of Markov features. The splicing detection performance is improved because the quantization method enlarges the discrimination of the probability distributions between the authentic and the spliced images. In this paper, we present two Markov feature selection algorithms. After quantization operation, we choose the sum of three directional Markov transition probability values at the corresponding position in the probability matrix as a first feature vector. For the second feature vector, the maximum value among the three directional difference values of the three color channels is used. A fixed number of features, regardless of the color channels and test datasets, are used in the proposed algorithm. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection. The average detection accuracy is over than 97% on three well-known splicing detection image datasets without the use of additional feature reduction algorithms. Furthermore, we achieve reasonable forgery detection performance for more modern and realistic dataset.     

Salient object detection via local saliency estimation and global homogeneity refinement

This paper presents a new hybrid approach for detecting salient objects in an image. It consists of two processes: local saliency estimation and global-homogeneity refinement. We model the salient object detection problem as a region growing and competition process by propagating the influence of foreground and background seed-patches. First, the initial local saliency of each image patch is measured by fusing local contrasts with spatial priors, thereby the seed-patches of foreground and background are constructed. Later, the global-homogeneous information is utilized to refine the saliency results by evaluating the ratio of the foreground and background likelihoods propagated from the seed-patches. Despite the idea is simple, our method can effectively achieve consistent performance for detecting object saliency. The experimental results demonstrate that our proposed method can accomplish remarkable precision and recall rates with good computational efficiency.     

Bayesian system identification via Markov chain Monte Carlo techniques

The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing accurate quantification of estimation error that is valid for arbitrary, and hence even very short length data records. The main innovation is the employment of the Metropolis-Hastings algorithm to construct an ergodic Markov chain with invariant density equal to the required posterior density. Monte Carlo analysis of samples from this chain then provides a means for efficiently and accurately computing posteriors for model parameters and arbitrary functions of them.     

Hidden Markov chain estimation and parameterisation via ICA-based feature-selection

We set out a methodology for the automated generation of hidden Markov models (HMMs) of observed feature-space transitions in a noisy experimental environment that is maximally generalising under the assumed experimental constraints. Specifically, we provide an ICA-based feature-selection technique for determining the number, and the transition sequence of the underlying hidden states, along with the observed-state emission characteristics when the specified noise model assumptions are fulfilled. In retaining correlation information between features, the method is potentially more general than the commonly employed Gaussian mixture model HMM parameterisation methods, to which we demonstrate that our method reduces when an arbitrary separation of features, or an experimentally-limited feature-space is imposed. A practical demonstration of the application of this method to automated sign-language classification is given, for which we demonstrate that a performance improvement of the order of 40% over naive Markovian modelling of the observed transitions is possible.     

Front-view vehicle detection by Markov chain Monte Carlo method

In this paper, we propose a new vehicle detection approach based on Markov chain Monte Carlo (MCMC). We mainly discuss the detection of vehicles in front-view static images with frequent occlusions. Models of roads and vehicles based on edge information are presented, the Bayesian problem's formulations are constructed, and a Markov chain is designed to sample proposals to detect vehicles. Using the Monte Carlo technique, we detect vehicles sequentially based on the idea of maximizing a posterior probability (MAP), performing vehicle segmentation in the meantime. Our method does not require complex preprocessing steps such as background extraction or shadow elimination, which are required in many existing methods. Experimental results show that the method has a high detection rate on vehicles and can perform successful segmentation, and reduce the influence caused by vehicle occlusion.