1,721,010 research outputs found
Particle swarm optimization for point pattern matching
No full text[[abstract]]The technique for point pattern matching (PPM) is essential to many image analysis and computer vision tasks. Given two point patterns, the PPM technique finds an optimal transformation for one point pattern such that a distance measure from the transformed point pattern to the other is minimized. This paper presents a new PPM algorithm based on particle swarm optimization (PSO). The set of transformation parameters is encoded as a real-valued vector called particle. A swarm of particles are initiated at random and fly through the transformation space for targeting the optimal transformation. The proposed algorithm is validated through both synthetic datasets and real fingerprint images. The experimental results manifest that the PSO-based method is robust against practical scenarios such as positional perturbations, contaminations, and drop-outs from the point sets. The PSO algorithm is also shown to be superior to a genetic algorithm and a simulated annealing algorithm on both effectiveness and efficiency. (c) 2005 Elsevier Inc. All rights reserved.[[note]]SC
Multilevel minimum cross entropy threshold selection based on particle swarm optimization
No full text[[abstract]]Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications. (C) 2006 Elsevier Inc. All rights reserved.[[note]]SC
A discrete particle swarm algorithm for optimal polygonal approximation of digital curves
No full text[[abstract]]Polygonal approximation of digital curves is one of the crucial steps prior to many image analysis tasks. This paper presents a. new polygonal approximation approach based on the particle swarm optimization (PSO) algorithm. Each particle represented as a binary vector corresponds to a candidate solution to the polygonal approximation problem. A swarm of particles are initiated and fly through the solution space for targeting the optimal solution. We also propose to use a hybrid version of PSO embedding a local optimizer to enhance the performance. The experimental results manifest that the proposed discrete PSO is comparable to the genetic algorithm, and it outperforms another discrete implementation of PSO in the literature. The proposed hybrid version of PSO can significantly improve the approximation results in terms of the compression ratio, and the results obtained in different runs are more consistent. (C) 2003 Elsevier Inc. All rights reserved.[[note]]SC
Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time
No full text[[abstract]]Ant colony optimization (ACO) is a meta-heuristic proposed to derive approximate solutions for computationally hard problems by emulating the natural behaviors of ants. In the literature, several successful applications have been reported for graph-based optimization problems, such as vehicle routing problems and traveling salesman problems. In this paper, we propose an application of the ACO to a two-machine flowshop scheduling problem. In the flowshop, no intermediate storage is available between two machines and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time. We first present a transformation of the scheduling problem into a graph-based model. An ACO algorithm is then developed with several specific features incorporated. A series of computational experiments is conducted by comparing our algorithm with previous heuristic algorithms. Numerical results evince that the ACO algorithm exhibits impressive performances with small error ratios. The results in the meantime demonstrate the success of ACO's applications to the scheduling problem of interest. (C) 2004 Elsevier Ltd. All rights reserved.[[note]]SC
Ant colony optimization for the nonlinear resource allocation problem
No full text[[abstract]]The nonlinear resource allocation problem addresses the important issue which seeks to find an optimal allocation of a limited amount of resource to a number of tasks for optimizing a nonlinear objective over the given resource constraint. Relevant literature has been focused on the use of mathematical programming approaches, few researches based on meta-heuristic algorithms have been conducted. In this paper we present an ant colony optimization algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we incorporate adaptive resource bounds to guide the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and a reliable performance guarantee is provided through a worst-case analysis. (c) 2005 Elsevier Inc. All rights reserved.[[note]]SC
An ant colony optimization algorithm for the minimum weight vertex cover problem
No full text[[abstract]]Given an undirected graph and a weighting function defined on the vertex set, the minimum weight vertex cover problem is to find a vertex subset whose total weight is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we introduce a meta-heuristic based upon the Ant Colony Optimization (ACO) approach, to find approximate solutions to the minimum weight vertex cover problem. In the literature, the ACO approach has been successfully applied to several well-known combinatorial optimization problems whose solutions might be in the form of paths on the associated graphs. A solution to the minimum weight vertex cover problem however needs not to constitute a path. The ACO algorithm proposed in this paper incorporates several new features so as to select vertices out of the vertex set whereas the total weight can be minimized as much as possible. Computational experiments are designed and conducted to study the performance of our proposed approach. Numerical results evince that the ACO algorithm demonstrates significant effectiveness and robustness in solving the minimum weight vertex cover problem.[[note]]SC
Adaptive relevance feedback model selection for content-based image retrieval
No full text[[abstract]]Owing to the rapid development in computer and network technologies, the volumes of modern image repositories have been overwhelming. In this context, traditional image retrieval based on textual indexing is laborious, thus inviting the implementation of content-based image retrieval (CBIR). Relevance feedback (RF) is an iterative procedure which refines the content-based retrievals utilizing the user's RF marked on retrieved results. Recent research has focused on RF model space optimisation. In this paper, we propose an adaptive RF model selection framework which automatically chooses the best RF model with proper parameter values for the given query. The proposed method combines the visual space and model space approaches in order to simultaneously perform two learning tasks, namely, the query optimisation and model optimisation. The particle swarm optimisation (PSO) paradigm is applied to assist the learning tasks. Experimental results tested on a real-world image database reveal that the proposed method outperforms several existing RF approaches using different techniques. The convergence behaviour of the proposed method is empirically analysed.[[note]]SC
Integrating relevance feedback techniques for image retrieval using reinforcement learning
No full text[[abstract]]Relevance feedback (RF) is an interactive process which refines the retrievals to a particular query by utilizing the user's feedback on previously retrieved results. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques in a content-based image retrieval system. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions significantly improves the performance. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model with the increasing-size of database.[[note]]SC
A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms
No full text[[abstract]]The wake effect is the key factor affecting the low efficiency of wind power production. It is very important to predict the relationship between the cost and the produced power for various wind-turbine placements under various wind speeds and directions. This paper proposes a GRASP-VNS algorithm for the optimal placement of wind turbines. Four different wind-farm conditions were considered: (a) uniform wind with single direction, (b) uniform wind with variable directions, (c) non-uniform wind with variable directions, and (d) non-uniform and variable-direction wind with land constraint. The proposed GRASP-VNS algorithm combines two well-known metaheuristics, GRASP and VNS, to create additional advantages in yielding the search trajectory. Intensive experiments assuming the four wind-farm conditions were performed. Statistical analyses show that the proposed GRASP-VNS algorithm significantly outperforms three existing GA-based methods. (C) 2012 Elsevier Ltd. All rights reserved.[[note]]SC
Content-based image retrieval using association rule mining with soft relevance feedback
No full text[[abstract]]With the rapid development of internet technology, the transmission and access of image items have become easier and the volume of image repository is exploding. An efficient and effective query reformulation is needed for finding the relevant images from the database. Relevance feedback (RF) is an interactive process which refines the retrieval results to a particular query by utilizing the user's feedback on previously retrieved images. Most of the existing approaches deal with hard feedback (relevant and nonrelevant) and focus on individual experience only. We propose to facilitate the use of soft feedback (involving excellent, fair, don't care, and bad) to better capture user's intention. To add this feature, all of the traditional RF techniques should be modified accordingly. Further, the meta-knowledge exploited from multiple users' experiences can improve the performance of future retrieval results. We propose a soft association rule mining algorithm to infer image relevance from the collective feedback. The number of association rules is kept minimum based on confidence quantization and redundancy detection. Also, binary search and best-first search techniques are implemented to expedite the process of relevance inference from the association rules. The proposed model provides a more flexible interface for relevance feedback and the experimental results manifest that the retrieval performance of the proposed model is better than that of traditional methods. (c) 2006 Elsevier Inc. All rights reserved.[[note]]SC
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