The Earth Mover's Distance as a Metric for Image Retrieval

We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.     

An efficient and robust algorithm for 3D mesh segmentation

This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.     

Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering

In this paper, we demonstrate how the differential Earth Mover's Distance (EMD) may be used for visual tracking in synergy with Gaussian mixtures models (GMM). According to our model, motion between adjacent frames results in variations of the mixing proportions of the Gaussian components representing the object to be tracked. These variations are computed in closed form by minimizing the differential EMD between Gaussian mixtures, yielding a very fast algorithm with high accuracy, without recurring to the EM algorithm in each frame. Moreover, we also propose a framework to handle occlusions, where the prediction for the object's location is forwarded to an adaptive Kalman filter whose parameters are estimated on line by the motion model already observed. Experimental results show significant improvement in tracking performance in the presence of occlusion.     

An efficient and robust artificial bee colony algorithm for numerical optimization

Artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms.     

Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)

An effective approach to phishing Web page detection is proposed, which uses Earth mover's distance (EMD) to measure Web page visual similarity. We first convert the involved Web pages into low resolution images and then use color and coordinate features to represent the image signatures. We use EMD to calculate the signature distances of the images of the Web pages. We train an EMD threshold vector for classifying a Web page as a phishing or a normal one. Large-scale experiments with 10,281 suspected Web pages are carried out to show high classification precision, phishing recall, and applicable time performance for online enterprise solution. We also compare our method with two others to manifest its advantage. We also built up a real system which is already used online and it has caught many real phishing cases     

An efficient algorithm for checking the robust stability of a polytope of polynomials

An efficient algorithm for checking the robust stability of a polytope of polynomials is proposed. This problem is equivalent to a zero exclusion condition at each frequency. It is shown that such a condition has to be checked at only afinite number of frequencies. We formulate this problem as aparametric linear program which can be solved by the Simplex procedure, with additional computations between steps consisting of polynomial evaluations and calculation of positive polynomial roots. Our algorithm requires a finite number of steps (corresponding to frequency checks) and in the important case when the polytope of parameters is a hypercube, this number is at most of orderO(m ³ n ²), wheren is the degree of the polynomials in the family andm is the number of parameters. Supported by NASA under Contract No. NCC2-477 and by a Charles Powell Foundation Grant.     

A robust and efficient algorithm for distributed compressed sensing

In this paper we present a new iterative greedy algorithm for distributed compressed sensing (DCS) problem based on the backtracking technique, which can reconstruct several input signals simultaneously by processing column by column of the compressed signals, even when the measurements are contaminated with noise and without any prior information of their sparseness. This makes it a promising candidate for many practical applications when the number of non-zero (significant) coefficients of a signal is not available. Our algorithm can provide a fast runtime while also offers comparably theoretical guarantees as the best optimization-based approach in both the noiseless and noisy regime. Numerical experiments are performed to demonstrate the validity and high performance of the proposed algorithm.     

基于嵌套EMD的钓鱼网页检测算法

网络钓鱼(Web phishing)以相似网站欺诈用户、骗取个人机密信息,已成为电子金融活动的重大威胁.对此,文中提出了一个钓鱼网页检测架构.在具体检测机制方面,提出了一个基于嵌套EMD(Nested Earth Mover's Distance)的网页相似度判定算法,对Web图像进行分割,抽取子图特征并构建网页的ARG (Attributed Relational Graph),在计算不同ARG属性距离的基础上,采用嵌套EMD方法计算网页的相似度,实现了对钓鱼网站的检测.实验结果表明,与国际现有研究成果相比,该算法具有较高的精度和较强的适应性.     

An evolutionary algorithm for robust regression

A drawback of robust statistical techniques is the increased computational effort often needed as compared to non-robust methods. Particularly, robust estimators possessing the exact fit property are NP-hard to compute. This means that—under the widely believed assumption that the computational complexity classes NP and P are not equal—there is no hope to compute exact solutions for large high dimensional data sets. To tackle this problem, search heuristics are used to compute NP-hard estimators in high dimensions. A new evolutionary algorithm that is applicable to different robust estimators is presented. Further, variants of this evolutionary algorithm for selected estimators—most prominently least trimmed squares and least median of squares—are introduced and shown to outperform existing popular search heuristics in difficult data situations. The results increase the applicability of robust methods and underline the usefulness of evolutionary algorithms for computational statistics.     

An efficient framework for generating robust adversarial examples

Recent studies show that deep neural networks (DNNs) suffer adversarial examples. That is, attackers can mislead the output of a DNN by adding subtle perturbation to a benign input image. In addition, researchers propose new generation of technologies to produce robust adversarial examples. Robust adversarial examples can consistently fool DNN models under predefined hyperparameter space, which can break through some defenses against adversarial examples or even generate physical adversarial examples against real-world applications. Behind these achievements, expectation over transformation (EOT) algorithm plays as the backbone framework for generating robust adversarial examples. Though EOT framework is powerful, we know little about why such a framework can generate robust adversarial examples. To address this issue, we do the first work to explain the principle behind robust adversarial examples. Then, based on the findings, we point out that traditional EOT framework has a performance problem and propose an adaptive sampling algorithm to overcome such a problem. By modeling the sampling process as classic Coupon Collector Problem, we prove that our new framework reduces the cost from $O\text{◂()▸}\left(n\ast \log \mathrm{}\left(n\right)\right)$ to $O\left(n\right)$, where $n$ denotes the number of sampling points. Under the view of computational complexity, the algorithm is optimal for this problem. The experimental results show that our algorithm can save up to 23% overhead in average. This is significant for black-box attack, where the cost is charged by the amount of queries.     

An efficient algorithm for Horn description

We give a new algorithm for computing a prepositional Horn CNF formula given the set of its models. Its running time is O(|R|n(|R|+n)), where |R| is the number of models and n that of variables, and the computed CNF contains at most |R|n clauses. This algorithm also uses the well-known closure property of Horn relations in a new manner.     

An efficient algorithm for positive realizations

We observe that successive applications of known results from the theory of positive systems lead to an efficient general algorithm for positive realizations of transfer functions. We give two examples to illustrate the algorithm, one of which complements an earlier result of [L. Benvenuti, L. Farina, An example of how positivity may force realizations of ‘large’ dimensions, Systems Control Lett. 36 (1999) 261–266]. Finally, we improve a lower-bound of [B. Nagy, M. Matolcsi, A lower-bound on the dimension of positive realizations, IEEE Trans. Circuits Syst. I 50 (2003) 782–784] to indicate that the algorithm is indeed efficient in general.     

An efficient algorithm for solving nonograms

Nonogram is one of logical games popular in Japan and Netherlands. Solving nonogram is a NP-complete problem. There are some related papers proposed. Some use genetic algorithm (GA), but the solution may get stuck in local optima. Some use depth first search (DFS) algorithm, the execution speed is very slow. In this paper, we propose a puzzle solving algorithm to treat these problems. Based on the fact that most of nonograms are compact and contiguous, some logical rules are deduced to paint some cells. Then, we use the chronological backtracking algorithm to solve those undetermined cells and logical rules to improve the search efficiently. Experimental results show that our algorithm can solve nonograms successfully, and the processing speed is significantly faster than that of DFS. Moreover, our method can determine that a nonogram has no solution.     

An efficient procedure for structural reliability-based robust design optimization

This paper focuses on the development of an optimization tool with the aim to obtain robust and reliable designs in short computational time. The robustness measures considered here are the expected value and standard deviation of the performance function involved in the optimization problem. When using these robustness measures combined, the search of optimal design appears as a robust multiobjective optimization (RMO) problem. Reliable design addresses uncertainties to restrict the structural probability of failure. The mathematical formulation for the reliability based robust design optimization (RBRDO) problem is obtained by adding a reliability based constraint into the RMO problem. As both, statistics calculations and the reliability analysis could be very costly, approximation technique based on reduced-order modeling (ROM) is also incorporated in our procedure. The selected ROM is the proper orthogonal decomposition (POD) method, with the aim to produce fast outputs considering structural non-linear behavior. Moreover, to obtain RBRDO designs with reduced CPU time we propose others developments to be added in the integrated tool. They are: Probabilistic Collocation Method (PCM) to evaluate the statistics of the structural responses and, also, an approximated reliability constraints procedure based on the Performance Measure Approach (PMA) for reliability constraint assessment. Finally, Normal-Boundary Intersection (NBI) or Normalized Normal-Constraint (NNC) multiobjective optimization techniques are employed to obtain fast and even spread Pareto robust designs. To illustrate the application of the proposed tool, optimization studies are conducted for a linear (benchmark) and nonlinear trusses problems. The nonlinear example consider different loads level, exploring the material plasticity. The integrated tool prove to be very effective reducing the computational time by up to five orders of magnitude, when compared to the solutions obtained via classical standard approaches.     

An efficient and robust contact algorithm for a compliant contact force model between bodies of complex geometry

Dynamic analysis of many mechanical systems often involves contacts among rigid bodies. When calculating the contact force with a compliant contact force model, a penetration depth and a contact reference frame (a contact point and normal and tangent directions) should be determined from the geometrical information of the rigid body surfaces. In order to improve the speed and robustness of the contact analysis, this paper proposes a contact search algorithm for surfaces composed of triangles. This algorithm is divided into two parts, the pre-search and the detailed search. In the pre-search, a bounding box tree and an overlap test are used to find intersecting triangle pairs, and triangle connectivity information is used to identify and separate multiple contact regions. Then an efficient and robust detailed search algorithm is proposed, where the penetration depth and contact reference frame are determined from the results of the pre-search. Finally, the contact force for each contact region is calculated from a modified compliant contact force model. Numerical examples are also presented to illustrate the accuracy and performance.     

图像特征匹配中一种高效的鲁棒估计算法

在图像特征匹配过程中,误匹配不可避免。提出一种新的基于拓扑约束（顺序约束和仿射不变约束）的外点去除算法,用于快速地去除图像粗匹配结果中的误配点。该算法 对随机采样集进行拓扑过滤,只对满足拓扑约束的采样集进行计算。实验表明,该算法相比于传统的鲁棒估计算法RANSAC和改进的PROSAC算法,大大提高了计算效率并保持很高的 计算精度,有助于提升图像匹配性能及3维重建的精度和鲁棒性。     

An ellipsoid algorithm for probabilistic robust controller design

In this paper, a new iterative approach to probabilistic robust controller design is presented, which is applicable to any robust controller/filter design problem that can be represented as an LMI feasibility problem. Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving LMIs. It transforms the initial feasibility problem to an equivalent convex optimization problem, which is subsequently solved by means of an iterative algorithm. While this algorithm always converges to a feasible solution in a finite number of iterations, it requires that the radius of a non-empty ball contained into the solution set is known a priori. This rather restrictive assumption is released in this paper, while retaining the convergence property. Given an initial ellipsoid that contains the solution set, the approach proposed here iteratively generates a sequence of ellipsoids with decreasing volumes, all containing the solution set. At each iteration a random uncertainty sample is generated with a specified probability density, which parameterizes an LMI. For this LMI the next minimum-volume ellipsoid that contains the solution set is computed. An upper bound on the maximum number of possible correction steps, that can be performed by the algorithm before finding a feasible solution, is derived. A method for finding an initial ellipsoid containing the solution set, which is necessary for initialization of the optimization, is also given. The proposed approach is illustrated on a real-life diesel actuator benchmark model with real parametric uncertainty, for which a robust state-feedback controller is designed.