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1.
In this article, an efficient and novel approach for video data association is developed. This new method is formulated as a search across the hypotheses space defined by the possible association among tracks and detections, carried out for each frame of a video sequence. The full data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc. To guarantee real‐time performance, a time limit is set for the search process explore alternative solutions. This time limit defines the upper bound of the number of evaluations depending on search algorithm efficiency. Estimation distribution algorithms are proposed as an efficient evolutionary computation technique to search in this hypothesis space. Finally, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 208–220, 2009  相似文献   

2.
The configuration space is a fundamental conc ept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this paper, we survey some of our recent work on solving two important challenges related to configuration spaces:(1) how to efficiently compute an approximate representation of high-dimensional configuration spaces; and(2) how to efficiently perform geometric proximity and motion planning queries in high-dimensional configuration spaces. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We also present parallel GPU-based algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPU-based parallel k-nearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning.  相似文献   

3.
This paper describes an efficient solution to parallelize software program instructions, regardless of the programming language in which they are written. We solve the problem of the optimal distribution of a set of instructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stages of our proposed genetic algorithm are: The choice of the initial population and its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, genetic algorithms are applied to the entire search space of the parallelization of the program instructions problem. This problem is NP-complete, so there are no polynomial algorithms that can scan the solution space and solve the problem. The genetic algorithm-based method is general and it is simple and efficient to implement because it can be scaled to a larger or smaller number of instructions that must be parallelized. The parallelization technique proposed in this paper was developed in the C# programming language, and our results confirm the effectiveness of our parallelization method. Experimental results obtained and presented for different working scenarios confirm the theoretical results, and they provide insight on how to improve the exploration of a search space that is too large to be searched exhaustively.  相似文献   

4.
针对传统相关匹配算法在无效搜索点耗费大量时间的状况,提出一种基于目标特征色调粒度判决来对相关搜索区域进行优化的目标跟踪方法。通过对HSV彩色空间进行量化并降维,构造出以色调信息为主的一维特征空间,对目标进行边缘检测,在目标轮廓区域内构造目标特征色调粒度集合,该粒度集中的颗粒包含色调值、色调面积以及像素距离瞄准中心平均距离等属性。采用粒度计算方法,对该集合与各搜索分块的色调粒度集合进行相似度计算,通过得出的各搜索块置信度,排除对目标特征色调信息不敏感的无效搜索区域。试验证明,该方法可以在保证精度的同时提高常规匹配算法的实时性能。  相似文献   

5.
Kim  H. Park  H. 《Communications, IET》2008,2(5):682-689
A reduced-complexity detector approaching maximum-likelihood (ML) detection performance is presented for the double space-time transmit diversity system. The proposed scheme exploits both the special structure of equivalent channel matrix and decision-feedback detection. This accounts for accomplishing near-ML or ML performance with significantly relieved computational loads. Moreover, to moderate the average complexity, several distance metric selection criteria are proposed. We can control performance and computational savings according to different distance metric selection rules. Numerical results show that the proposed detector requires significantly fewer computations than that of the Schnorr-Euchner sphere-decoding algorithm in terms of both the worst-case and the average complexity.  相似文献   

6.
Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.  相似文献   

7.
Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n3) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that the clustering results obtained are not ideal. Accordingly, in order to obtain more information from each base partition and thus improve the clustering performance, we propose a new late fusion multi-view clustering algorithm with a computational complexity of O(n2). Experiments on several commonly used datasets demonstrate that the proposed algorithm can reach quickly convergence. Moreover, compared with other late fusion algorithms with computational complexity of O(n), the actual time consumption of the proposed algorithm does not significantly increase. At the same time, comparisons with several other state-of-the-art algorithms reveal that the proposed algorithm also obtains the best clustering performance.  相似文献   

8.
Owing to the importance of video surveillance in the public area, tracking finds significant applications using computer vision algorithms to observe the activity of human. In tracking, multi-object tracking is an active research to analyse and detect the activity of anomalies in the crowded scenes. Accordingly, different multi-object tracking algorithms are proposed in the literature to track the human behaviour of the crowded scenes. In this paper, we have presented a zero-stopping criteria-based hybrid tracking algorithm for high-dense crowd videos. Here, head objects are detected using the proposed objective function which considers both colour and texture property of videos. Then, tracking based on motion is performed using the proposed HSIM measure which includes structural similarity (SSIM) and the proposed similarity function. Along with, the data prediction model, exponential weighted moving average (EWMA), is also utilised to track the spatial location of human objects. These two tracking models are then hybridised to obtain the final tracked output. The experimentation is performed with three marathon sequences and the performance is evaluated with particle filtering-based algorithm using tracking number, tracking distance and optimal subpattern assignment metric (OSPA).  相似文献   

9.
Considering that no single algorithm available is universal in color constancy, we propose an effective combination approach using a texture-based matching strategy and a local regression with prior-knowledge regularization. To represent the images, we construct a texture pyramid using an integrated Weibull distribution. Then we define an image similarity measure to search for the K most similar images of the test image. To combine the single algorithms, we integrate prior knowledge into a regularized local regression in a decorrelated color space. Regression weights are obtained on these similar images, and the regularization is implemented by the frequency ratio of the best single algorithm. Experiments on two real world datasets show our approach outperforms the state-of-the-art single algorithms and popular combination approaches with a performance increase of at least 29% compared to the best-performing single algorithm w.r.t median angular error.  相似文献   

10.
Attribute discretization and reduction are two key issues in rough set theory. However, almost all previous studies consider them as two separate steps, which can not capture an inherent relationship between them. In this paper, a bi-objective optimization problem is constructed for simultaneous attribute discretization and reduction. A novel compromise-based ant colony algorithm (CACA) for simultaneously solving attribute discretization and reduction is proposed, which adopts a distance metric to stepwise approach the ideal solution. To improve efficiency of the proposed method, both the cut information and attribute information are adopted to dynamically calculate heuristic information, and a local search strategy is also embedded. The grade of nature spearmint essence (NSE), wine and glass classification problems are used as three test datasets to demonstrate the validity of the proposed CACA. Furthermore, the proposed method is applied to two toxicity mechanism classification problems: the classification of three narcosis mechanisms of aquatic toxicity for 194 organic compounds and the classification of four action modes of 221 phenols. The obtained results illustrate that the proposed method has better prediction performance than linear discriminant analysis, radial basis function neural network and support vector machine.  相似文献   

11.
This paper proposes two new differential evolution algorithms (DE) for solving the job shop scheduling problem (JSP) that minimises two single objective functions: makespan and total weighted tardiness. The proposed algorithms aim to enhance the efficiency of the search by dynamically balancing exploration and exploitation ability in DE and avoiding the problem of premature convergence. The first algorithm allows DE population to simultaneously perform different mutation strategies in order to extract the strengths of various strategies and compensate for the weaknesses of each individual strategy to enhance the overall performance. The second algorithm allows the whole DE population to change the search behaviour whenever the solutions do not improve. This study also introduces a modified local mutation operation embedded in the two proposed DE algorithms to promote exploitation in different areas of the search space. In addition, a local search technique, called Critical Block (CB) neighbourhood, is applied to enhance the quality of solutions. The performances of the proposed algorithms are evaluated on a set of benchmark problems and compared with results obtained from an efficient existing Particle Swarm Optimisation (PSO) algorithm. The numerical results demonstrate that the proposed DE algorithms yield promising results while using shorter computing times and fewer numbers of function evaluations.  相似文献   

12.
This paper addresses the scheduling problems in a hybrid flowshop with two objectives of minimising the makespan and total tardiness. Since this problem is NP-hard, evolutionary algorithms based on the genetic algorithm (GA) namely; BOGAW, BOGAC, BOGAT, and BOGAS are proposed for searching the Pareto-optimal frontier. In these algorithms, we propose to generate a section of solutions for the next generation using a neighbourhood search structure on the best individual in each generation. The selection procedure selects the best chromosome based on an evaluation mechanism used in the algorithm (i.e., weighted sum, crowding distance, TOPSIS and single-objective). The aim of this paper is to clarify that the cited characteristic is efficient and it enhances the efficiency of algorithms. Therefore, we perform a comparison between the proposed algorithms to find the best alternative. Data envelopment analysis is used to evaluate the performance of approximation methods. The obtained result from the comparison shows that, BOGAC is the more efficient. To continue, since the efficiency of our idea is not clear, we compare our efficient algorithm with other efficient algorithms in the literature (namely PGA-ALS and MOGLS). The final persuasive results support the idea that BOGAC in comparison with PGA-ALS and MOGLS is more effective and efficient.  相似文献   

13.
The standardized residual sum of squares (STRESS) index was used to reevaluate four experimental datasets employed during the development of CIEDE2000, the current CIE recommended color-difference formula. This index enables statistical inferences not achievable by other metrics used commonly for performance evaluation. It was found that CIEDE2000 was statistically superior at a 95% confidence level to either CIE94, the previous recommended equation by the CIE, or the simple Euclidean distance in CIELAB, DeltaE*ab. Recent formulas based on the CIECAM02 color-appearance space and chroma-compressed variants of CIELAB were also evaluated and found to have only slightly reduced performance compared with CIEDE2000. These formulas have the advantage of simplicity and easier interpretation when used for quantifying color accuracy. Finally, each experimental dataset was evaluated separately rather than weight averaged as used during the development of CIEDE2000. Significant differences were found between datasets, suggesting that combining datasets may obscure important differences and that the practice of parameter optimization during formula development using combined data is likely suboptimal.  相似文献   

14.
Simple bit loading algorithm for OFDM-based systems   总被引:1,自引:0,他引:1  
A simple and efficient bit loading algorithm for orthogonal frequency division multiplex-based systems in frequency selective environments is presented here. The proposed algorithm minimises the total transmitted power for a given bit rate and probability of error. Simulation results show that the proposed algorithm has approximately the same performance as the optimal algorithms with much less complexity. When compared with suboptimal algorithms, the computational complexity is comparable while the overall performance is much closer to the optimum solution  相似文献   

15.
Whale optimization algorithm (WOA) is a new population-based metaheuristic algorithm. WOA uses shrinking encircling mechanism, spiral rise, and random learning strategies to update whale’s positions. WOA has merit in terms of simple calculation and high computational accuracy, but its convergence speed is slow and it is easy to fall into the local optimal solution. In order to overcome the shortcomings, this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms, designs the average distance from itself to other whales as an adaptive neighborhood radius, and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies. The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution. A new whale optimization algorithm (HMNWOA) is proposed. The proposed algorithm inherits the global search capability of the original algorithm, enhances the exploitation ability, improves the quality of the population, and thus improves the convergence speed of the algorithm. A feature selection algorithm based on binary HMNWOA is proposed. Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection. The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features, and ensures that HMNWOA has strong search ability in the search feature space.  相似文献   

16.
Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO’s optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.  相似文献   

17.
廖波 《工业工程》2011,14(1):53-57
针对传统调度算法寻优效率低的弱点,从MES功能出发,将其调度功能单独抽出,提出了基于聚类的粒子群优化算法,将聚类用于粒子群搜索空间的改进。仿真结果表明了该算法的有效性。  相似文献   

18.
Parametric variation and optimisation using genetic algorithms employing single and multi-objective functions are proposed for the optimisation of a structural steel/composite connection. The joint in marine applications is the connection between the steel hull and the composite superstructure of a naval vessel. A baseline joint is defined and all parametric variations and optimised joints are compared to this. The parametric results provided design curves of the joint performance determined from the weight, Von Mises stress in the adhesive and the global stiffness indicating performance sensitivity to specific changes in the joint geometry.

The results indicated that the parametric variations can lead to an improvement in the performance but high levels of human interaction are required to make a combined improvement to the performance. The use of genetic algorithms provided an efficient method of searching the design space for an optimal joint. The single objective function provides an excellent reduction in the weight and maintaining or improving the performance of the joint to in-plane compressive loading. The use of the multi-objective function whereby a weighting was applied to the weight, stress and stiffness performance criteria proved extremely successful in further optimising the joint. The use of genetic algorithms has been demonstrated to efficiently search the complex design space of a structural connection and the use of multi-objective functions as the most effective selection method.  相似文献   


19.
Ueno  Maomi  Yamazaki  Takahiro 《Behaviormetrika》2008,35(2):137-158

This paper proposes a collaborative filtering method for massive datasets that is based on Bayesian networks. We first compare the prediction accuracy of four scoring-based learning Bayesian networks algorithms (AIC, MDL, UPSM, and BDeu) and two conditional-independence-based (Cl-based) learning Bayesian networks algorithms (MWST, and Polytree-MWST) using actual massive datasets. The results show that (1) for large networks, the scoring-based algorithms have lower prediction accuracy than the CI-based algorithms and (2) when the scoring-based algorithms use a greedy search to learn a large network, algorithms which make a lot of arcs tend to have less prediction accuracy than those that make fewer arcs. Next, we propose a learning algorithm based on MWST for collaborative filtering of massive datasets. The proposed algorithm employs a traditional data mining technique, the “a priori” algorithm, to quickly calculate the amount of mutual information, which is needed in MWST, from massive datasets. We compare the original MWST algorithm and the proposed algorithm on actual data, and the comparison shows the effectiveness of the proposed algorithm.

  相似文献   

20.
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