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Mass classification is an important problem in breast cancer diagnosis. In this paper, we investigated the classification of masses with feature selection. Based on the initial contour guided by radiologist, level set algorithm is used to deform the contour and achieves the final segmentation. Morphological features are extracted from the boundary of segmented regions. Then, important features are extracted based on mutual information criterion. Linear discriminant analysis and support vector machine are investigated for the final classification. Mammography images from DDSM were used for experiment. The method achieved an accuracy of 86.6% with mutual information based feature selection and SVM classifier. The experimental result shows that mutual information based feature selection is useful for the diagnosis of masses.

Recently, WS-BPEL has gradually become the basis of a standard for web service description and composition. However, WS-BPEL cannot efficiently describe distributed workflow services for lacking of special expressive power and formal semantics. This paper presents a novel method for modeling distributed workflow service composition with Concurrent TRansaction logic (CTR). The syntactic structure of WS-BPEL and CTR are analyzed, and new rules of mapping WS-BPEL into CTR are given. A case study is put forward to show that the proposed method is appropriate for modeling workflow business services under distributed environments.

Dimension reduction is critical in many areas of pattern classification and machine learning and many algorithms have been proposed. Pairwise Covariance-preserving Projection Method (PCPM) was an effective dimension reduction which maximizes the class discrimination and also preserves approximately the pairwise class covariances. A shortcoming of PCPM is that it can only be applied when all labels are given, thus a typical supervised method. Semi-supervised has attracted much attention in recent years since they can utilize both labeled and unlabeled data. In this paper, we extend PCPM to semi-supervised setting. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. The target optimization problem involved can be solved efficiently by eigenvalue decomposition. Experimental results on several datasets demonstrate the effectiveness of our method.

Traditional monochrome image colorization techniques require considerable user interaction and a lot of time. The segment-based colorization works fast but at the expense of detail loss because of the large segmentation; while the optimization based method looks much more continuous but takes longer time. This paper proposed a novel approach: Segmentation colorization based on random walks, which is a fast segmentation technique and can naturally handle multi-label segmentation problems. It can maintain smoothness almost everywhere except for the sharp discontinuity at the boundaries in the images. Firstly, with the few seeds of pixels set manually scribbled by the user, a global energy is set up according to the spatial information and statistical grayscale information. Then, with random walks, the global optimal segmentation is obtained fast and efficiently. Finally, a banded graph cut based refine procedure is applied to deal with ambiguous regions of the previous segmentation. Several results are shown to demonstrate the effectiveness of the proposed method.

Dimension reduction is very important for applications in data mining and machine learning. Dependence maximization based supervised feature extraction (SDMFE) is an effective dimension reduction method proposed recently. A shortcoming of SDMFE is that it can only use labeled data, and does not work well when labeled data are limited. However, in many applications, it is a common case. In this paper, we propose a novel feature extraction method, called Semi-Supervised Dependence Maximization Feature Extraction (SSDMFE), which can utilize simultaneously both labeled and unlabeled data to perform feature extraction. The labeled data are used to maximize the dependence and the unlabeled data are used as regulations with respect to the intrinsic geometric structure of the data. Experiments on several datasets are presented and the results demonstrate that SSDMFE achieves much higher classification accuracy than SDMFE when the amount of labeled data are limited.

LTSA (local tangent space alignment) is a recently proposed method for manifold learning, which can efficiently learn nonlinear embedding low-dimensional coordinates of high-dimensional data, and can also reconstruct high dimensional coordinates from embedding coordinates. But it ignores the label information conveyed by data samples, and can not be used for classification directly. In this paper, a transductive manifold classification method, called QLAT (LDA/QR and LTSA based Transductive classifier) is presented, which is based on LTSA and TCM-KNN (transduction confidence machine-k nearest neighbor). In the algorithm, local low-dimensional coordinates is constructed using 2-stage LDA/QR method, which not only utilize the label information of sample data, but also conquer the singularity problem of traditional LDA, then the global low-dimensional embedding manifold is obtained by local affine transforms, finally TCM-KNN method is used for classification on the low-dimensional manifold. Experiments on labeled and unlabeled mixed data set illustrate the effectiveness of the method.

Several algorithms have been proposed to analysis the structure of high-dimensional data based on the notion of manifold learning. They have been used to extract the intrinsic characteristic of different type of high-dimensional data by performing nonlinear dimensionality reduction. Most of them operate in a “batch” mode and cannot be efficiently applied when data are collected sequentially. In this paper, we proposed an incremental version (ILTSA) of LTSA (Local Tangent Space Alignment), which is one of the key manifold learning algorithms. Besides, a landmark version of LTSA (LLTSA) is proposed, where landmarks are selected based on LASSO regression, which is well known to favor sparse approximations because it uses regularization with l1 norm. Furthermore, an incremental version (ILLTSA) of LLTSA is also proposed. Experimental results on synthetic data and real word data sets demonstrate the effectivity of our algorithms.

Grid workflow applications are emerging as an important new alternative to develop truly distributed applications for the grid platform. The modeling of grid workflow system is a challenging task, due to the high degree of autonomy and heterogeneity of the cooperative organizations under distributed environments. This paper presents a formal specification methodology for grid workflow modeling which is founded upon the Pi-Calculus process algebra. This method works well for characterizing the behaviors and interactions of the grid workflow processes that belong to different organizations in terms of the semantics of Pi-Calculus.

This paper presents an implementation of Croitoru's genetic algorithm for graph coloring problem, and some necessary modification and simplifying are made by using DNA operations. In this algorithm, each vertex and edge is encoded with a series of encodings incorporating position information, and the initial diverse candidate population is generated using POA. One crossover operator, two mutation operators, evaluation and selection operators are all implemented using basic operations on DNA. It is shown that the algorithm can be implemented with space complexity much decreased and time complexity O(mn2) to get a new generation, where n is the number of vertices and m is the number of edges. Moreover, borrowing ideas from the above implementation, an algorithm for Maximal Clique problem is also presented.

Phase field model has been well acknowledged as an important method for image segmentation. This paper discussed the problem of jacquard image segmentation by approaching the phase field paradigm from a numerical approximation perspective. For fuzzy theory provides flexible and efficient techniques for dealing with conflicting optimization probelms, a novel fuzzy optimization algorithm for numerical solving of the model was proposed. To achieve global minimum of the model, a multi-start fuzzy strategy which combined a local minimization procedure with genetic algorithm was enforced. As the local minimization procedure does not guarantee optimality of search process, several random starting points need to be generated and used as input into global search process. In order to construct powerful search procedure by guidance of global exploration, genetic algorithm was applied to scatter the set of quasi-local mimizers into global positions. Experimental results show that the proposed algorithm is feasible, and reaches obvious effects in terms of jacquard image segmentation.

Phase field models provide a well-established framework for the mathematical description of free boundary problems for image segmentation. In phase field models interfaces represent edges of jacquard images and the determination of the edges of jacquard images is the main goal of image segmentation. In this paper, the phase field model was applied to segment and recognize pattern structures of jacquard images. The segmentation was performed in two major steps. Firstly, a pattern extraction and representation was performed by an adaptive mesh generation scheme. For the conjugate gradient method has been successfully used in solving the symmetric and positive definite systems obtained by the finite element approximation of energy functionals, a novel conjugate gradient algorithm was adapted to the minimization of energy functional of discrete phase model. Experimental results show efficiency of our approach.

Automatic pattern segmentation of jacquard images is a challenging task for jacquard pattern analysis. In this paper, the phase field model was introduced to extract specific pattern structures within jacquard images. A novel fuzzy optimization method, namely, Multi-start Fuzzy Optimization Method (MSFOM) was proposed for numerical solving of the phase field model. The proposed method was a hybrid algorithm combining fuzzy logic and genetic algorithms, which was able to find global minimum of the phase field model with low computational cost. Experimental results on synthetic and jacquard images demonstrate the efficacy of the proposed method.