Showing papers by "Shu-Chuan Chu published in 2006"

Cat swarm optimization

Abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

Cat swarm optimization

Abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

Timetable Scheduling Using Particle Swarm Optimization

Abstract: In timetable scheduling problems, examination subjects must be slotted to certain times that satisfy several of constraints. They are NP-completeness problems, which usually lead to satisfactory but suboptimal solutions. As PSO has many successful applications in continuous optimization problems, the main contribution of this paper is to utilize PSO to solve the discrete problem of timetable scheduling. Experimental results confirm that PSO can be utilized to solve discrete problem as well.

Intelligent Parallel Particle Swarm Optimization Algorithms

Abstract: Some social systems of natural species, such as flocks of birds and schools of fish, possess interesting collective behavior. In these systems, globally sophisticated behavior emerges from local, indirect communication amongst simple agents with only limited capabilities. In an attempt to simulate this flocking behavior by computers, Kennedy and Eberthart (1995) realized that an optimization problem can be formulated as that of a flock of birds flying across an area seeking a location with abundant food. This observation, together with some abstraction and modification techniques, led to the development of a novel optimization technique particle swarm optimization. Particle swarm optimization has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. Some communication strategies for PPSO are discussed in this work, which can be used according to the strength of the correlation of parameters. Experimental results confirm the superiority of the PPSO algorithms.

Image Texture Segmentation with Ant Colony Systems

Abstract: A new scheme for texture segmentation based on Ant Colony Systems (ACS) is proposed in this paper. Texture segmentation is one of the important branches in image pattern recognition, which provides usefulness in many applications. Until now, how to find an effective way for accomplishing texture segmentation in practical applications is still a major task. In this paper, we employ wavelet coefficients and characteristics of different subbands to serve as the basis of characteristic vectors, and we use three feature-extraction elements, namely, the extrema, entropy, and energy, to compose the characteristic vector. To alleviate segmentation fragments caused from the information in high frequency bands of texture images, we integrate the fourth element, the mean variance, into the characteristic vector. Finally, we use ACS to find a trade-off between texture segmentation and fragments. Simulation results demonstrate the effectiveness and practicability of the proposed algorithm.

Discriminant Feature Fusion Strategy for Supervised Learning

Abstract: An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.

A Novel Matrix Norm Based Gaussian Kernel for Feature Extraction of Images

Abstract: Gaussian kernel is widely used in Support Vector Machines and many other kernel methods, and it is most often deemed to provide a local measure of similarity between vectors, which causes large storage requirements and large computational effort for transforming images to vectors owing to its viewing images as vectors. A novel matrix norm based Gaussian kernel (M-Gaussian kernel) which views images as matrices is proposed to solve the problem. Experiments conducted on ORL face database show the effectiveness of the proposed M-Gaussian kernel.

VQ-Based Watermarking Techniques

Abstract: . New methods for digital image watermarking based on the characteristics of vector quantization (VQ) are proposed. In contrast with the conventional watermark embedding algorithms to embed only one watermark at a time into the original source, we present several algorithms, including embedding one binary watermark, three binary watermarks, and the grey-level watermark, for copyright protection. The embedding and extraction processes are efficient for implementing with the conventional VQ techniques, and they can be accomplished in parallel to shorten the processing time. After embedding, the embedder would output one watermarked reconstruction image and secret keys associated with the embedded watermarks. These secret keys are then registered to the third party to preserve the ownership of the original source in order to prevent the attackers from inserting counterfeit watermarks. Simulation results show that under no attacks, the em-bedded watermarks could be perfectly extracted. If there are some intentional attacks such as VQ or JPEG compression, image cropping, spatial filtering, or geometric attacks in our simulation, all the watermarks could survive to protect the copyrights. Therefore, we are able to claim the robustness, usefulness, and ease of implementation of our algorithm.Keywords: watermarking, vector quantization, attacking scheme

The Attacking Method for DC Components Watermarking

Abstract: In this paper we present two methods to remove the watermark from the carrier image, which is embedded in dc components of the discrete cosine transform based image using the method presented by Huang et al [1] The first method, which we present, removes the watermark via varying the energies in frequency domain and redeeming the luminance in spatial domain; the second method we present removes the watermark through diversifying the intensity of dc components in transform domain Both the two methods can remove the watermark and avoid to be detected that the image was hidden a watermark by the detection criterion In addition, though we cleaned the watermark from the watermarked image, the PSNR of the cleaned image (compared with the original carrier image) is still high