Zhiwei Ye

Automatic Threshold Selection Based on Particle Swarm Optimization Algorithm

Image segmentation is a long-term difficult problem, which hasnpsilat been fully solved. Thresholding is one of the most popular algorithms. Particle swarm optimization (PSO) was recently proposed algorithm, which has been successfully applied to solve many optimization problems. Based on the analysis of Otsu threshold selection can be viewed as a continuous optimization problem. Thus, a new method to select image threshold automatically based on PSO algorithm is employed in the paper. The performance of this algorithm is compared with Otsu, and experimental results show that PSO algorithm can reveal very encouraging results in terms of the quality of solution found and the processing time required.

A feature selection method based on modified binary coded ant colony optimization algorithm

Graphical abstractDisplay Omitted HighlightsWe propose a novel binary coded ant colony algorithm by blending of GA and BACO.The proposed algorithm is adopted to handle with the problem of feature selection.Results show the method outperforms GA, BPSO, BACO, ABACO, BDE in feature selection. Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

An ETL Services Framework Based on Metadata

This paper first analyzed the problems of existing ETL tools, and proposed an ETL service model based on metadata, and then summarizes the types of metadata and their application scope. Based on this ETL service model, a concrete ETL service framework was put forward; many important services were also discussed, such as metadata management service, metadata definition services, ETL transformation rules service, process definition service, SQL code generation and optimization services, process control services and so on. At last, definition method and related algorithms of ETL rules are designed and analyzed. Practice has proved that the model and framework proposed in this paper can improve the ETL efficiency to a large extent.

Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm

Support vector machine (SVM) is one of the most successful classifiers for remote sensing image classification. However, the performance of SVM is mainly dependent on its parameters; in addition, for remote sensing images with high-dimensional features, feature redundancy will have a major influence on the classification efficiency and accuracy. Feature selection and parameter optimization are two important factors for improving the performance of SVM and are traditionally solved separately. In fact, these two issues are affected by each other, so to obtain the better classification performance, selection of the optimal feature subset and tuning of SVM parameters should be considered simultaneously, as they both belong to the combinatorial optimization problem and could be handled with evolutionary algorithms or swarm intelligence algorithms. In this paper, a remote sensing image classification technique based on the optimal SVM is proposed, in which the parameters of SVM and feature selection are handled integrally by a modified coded ant colony optimization algorithm combined with genetic algorithm. The results are compared with other evolutionary algorithms and swarm intelligence algorithms, such as genetic algorithm (GA), binary-coded particle swarm optimization (BPSO) algorithm, binary-coded ant colony optimization (BACO) algorithm, binary-coded differential evolution (BDE) algorithm, and binary-coded cuckoo search (BCS) algorithm. It is demonstrated that the proposed method is robust, adaptive and exhibits the better performance than the other methods involved in the paper in terms of fitness values, so could be suitable for some practical applications.

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

Automatic Threshold Selection Based on Artificial Bee Colony Algorithm

The goal of image segmentation is to cluster pixels into¿@salient image regions, it is the most significant step in image analysis. Thresholding is a simple but effective tool to separate objects from the background, which is one of the most popular algorithms. The artificial bee colony algorithm (ABC) is a recently presented meta-heuristic algorithm, which has been successfully applied to solve many optimization problems. As a matter of fact the classical Otsu threshold selection can be viewed as an optimization problem. Hence, this paper introduces a new method to select image threshold automatically based on ABC algorithm. In the end, the proposed method has been implemented and tested on several images. Experiments results show that proposed method performs well which is a feasible method to help select optimum threshold.

Comparison with Several Fuzzy Trust Methods for P2P-based System

Due to Fuzzy logic can help in handling the imprecise nature and uncertainty of trust, some papers use several fuzzy logic methods(such as fuzzy inference and fuzzy comprehensive evaluation) to tackle trust modeling for P2P-based system. However, nobody summarizes difference of these fuzzy logic methods. In this paper, authors present a general fuzzy trust problem domain for P2P-based system, and compare Fuzzy Comprehensive Evaluation method, Fuzzy Rank-ordering method, and Fuzzy Inference method through a concrete paradigm. Results demonstrate that different fuzzy trust method for P2P-based system may deduce different fuzzy results to the same question. Through the paradigm, we can conclude that: To the question of general P2P Fuzzy Trust problem domain, complexity and scalability of Fuzzy Comprehensive Evaluation Method for P2P-based trust system is superior to that of Fuzzy Rank-ordering Method and that of Fuzzy Inference Method.

An image threholding approach based on cuckoo search algorithm and 2D maximum entropy

The image thresholding approach based on the basis of 2-D maximum entropy has better segmentation performance by the use of local space information of pixels, but it is unpractical for heavy computation required by this method. In the paper, an image segmentation technology based on cuckoo search and 2-D maximum entropy is presented, which views the seeking of 2-D maximum entropy of the image as a function optimization problem and uses the behavior of the obligate brood parasitism of some cuckoo species to simulate the process of searching optimal threshold. Furthermore, a local search strategy is employed to improve the results in the cuckoo search algorithm. The experimental results proves that compared with 2-D maximum entropy thresholding optimized with genetic algorithm, differential evolution algorithm and particle swarm optimization algorithm, the proposed method is able to get the optimal thresholds quickly and with a higher probability to get optimal threshold, which is a fast and robust image segmentation method.

A New Model for P2P Traffic Identification Based on DPI and DFI

Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. P2P traffic identification has two promising approaches: DPI (Deep Packet Inspection) and DFI (Deep Flow Inspection). The purpose of the paper is to solve how to carry out research on how to take advantage of merits of these methods, avoid their defects, and assemble the two methods as a whole. The paper brings forward a new model for P2P traffic identification based on DPI and DFI. This model puts DPI method and DFI method together to recognize P2P traffic. The model comprises five parts: Traffic Collect Module, DPI Module, DFI Module, Coordinated Module between DPI and DFI, and Evaluation Module. DPI and DFI work together in this model not only enhance identification veracity of P2P traffic, but also expand identification extend of P2P traffic effectively. KeywordsPeer-to-Peer; Deep Packet Inspection; Deep Flow Inspection

A band selection method for airborne hyperspectral image based on chaotic binary coded gravitational search algorithm

Band selection is one of the most important topics in hyperspectral image classification for irrelevant band information and the high correlation between the adjacent bands. The main concern is to obtain the compact and effective bands to classify the image with the least impact for the classification accuracy. In general, band selection could be seen as a combinatorial optimization problem through defining an objective function based on the number of bands and classification accuracy. Therefore, in the paper, a novel band selection method based on a chaotic binary coded gravitational search algorithm (CBGSA) is proposed to reduce the dimensionality of airborne hyperspectral images. The proposed method is also compared with that of genetic algorithm (GA), binary coded particle swarm optimization (BPSO) algorithm, binary coded differential evolution (BDE) algorithm and binary coded cuckoo search (BCS) algorithm on some airborne hyperspectral images; furthermore, it is also compared with some other existing techniques such as Relief-F algorithm, minimum Redundancy Maximum Relevance (mRMR) criterion, and the optimum index (OI) criterion for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and might be applied for practical work of airborne hyperspectral image classification.