205396964
A distance-based separator representation for pattern classification
In pattern classification, Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are commonly used to reduce the dimensionality of input feature space. However, there exist some problems such that how many eigen vectors are needed to be the most effective in the transformation map as well as the lack of optimal separability in low dimensional data. In this paper, we present a new distance-based separator representation to solve these problems. The representation frame structure keeps adjustment pertaining to the problem complexity, and its dimensionality corresponds to the number of classes. Experimental results show that the new representation outperforms the PCA and LDA representations in multi-class classification and low-dimensional classification.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
8216383
Locating object contours in complex background using improved snakes
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds.
1936453
A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing
Gridding, the first step in spotted DNA microarray image processing, usually requires human intervention to achieve acceptable accuracy. We present a new algorithm for automatic gridding based on hierarchical refinement to improve the efficiency, robustness and reproducibility of microarray data analysis. This algorithm employs morphological reconstruction along with global and local rotation detection, non-parametric optimal thresholding and local fine-tuning without any human intervention. Using synthetic data and real microarray images of different sizes and with different degrees of rotation of subarrays, we demonstrate that this algorithm can detect and compensate for alignment and rotation problems to obtain reliable and robust results.
29748731
Support vector machine networks for multi-class classification
The support vector machine (SVM) has recently attracted growing interest in pattern classification due to its competitive performance. It was originally designed for two-class classification, and many researchers have been working on extensions to multiclass. In this paper, we present a new framework that adapts the SVM with neural networks and analyze the source of misclassification in guiding our preprocessing for optimization in multiclass classification. We perform experiments on the ORL database and the results show that our framework can achieve high recognition rates.
16445417
Multiple Viewpoints Based Ontology Integration
Ontology integration is a focus on ontology application field. Ontology can be viewed as a kind of software product. Ontology integration needs to be directed by methodology. In many applications, we need to integrate existed ontologies for a unified ontology for application requirements. The ontology to be integrated can be viewed as a viewpoint of the unified ontology. A multiple-viewpoints-based ontology integration approach is introduced by multiple viewpoints theory in requirement engineering. We define ontology viewpoint by characters of ontology and use conceptual graph to represent semantic in ontology. We discuss the inconsistency checking inner ontology viewpoint and among ontology viewpoints. At last, we use a concept lattice to construct the concept hierarchy.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
26145575
Domain decomposition methods for linear and semilinear elliptic stochastic partial differential equations
In this paper, we study several overlapping domain decomposition methods for the numerical solutions of some linear and semilinear elliptic stochastic partial differential equations discretized by the finite element methods. In particular, we show that the algorithms converge and the convergence rates are independent of the finite element mesh parameter, as well as the number of subdomains used in the domain decomposition.
973036
Accelerated Convergence Using Dynamic Mean Shift
Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it can be shown that both the standard and dynamic mean shift algorithms converge to the same optimal solution. However, while standard mean shift only has linear convergence, the dynamic mean shift algorithm has superlinear convergence. Experiments on color image segmentation show that dynamic mean shift produces comparable results as the standard mean shift algorithm, but can significantly reduce the number of iterations for convergence and takes much less time.
22845738
A Policy-Driven Approach for Software-as-Services Customization
Software as services (SaS) is emerging as a new application software delivery approach, and has extensively leveraged Web services and other SOA concepts and technologies. Compared to the traditional ones, SaS has the following distinctive features: application delivery over Internet, usage based pricing mechanism, etc. Potentially, this approach can significantly reduce the total cost of ownership of application software. However, it does have a number of unique issues that include service customization, multi-tenancy, complex billing model, etc. If not handled properly, these issues can result in ad-hoc and manual service consumption practices, which could eliminate the benefits of the SaS approach. In this paper, we are going to specifically deal with the service customization issue. To that end, we proposed a novel SaS customization policy and a supporting framework that is realized through a design-time tooling and a runtime environment. We also provided a case study to verify the effectiveness of the proposed approach.
18105952
Adapting Prime Number Labeling Scheme for Directed Acyclic Graphs
Directed Acyclic Graph(DAG) could be used for modeling subsumption hierarchies. Several labeling schemes have been proposed or tailored for indexing DAG in order to efficiently explore relationships in such hierarchy. However few of them can satisfy all the requirements in response time, space, and effect of updates simultaneously. In this paper, the prime number labeling scheme is extended for DAG. The scheme invests intrinsic mapping between integer divisibility and subsumption hierarchy, which simplifies the transitive closure computations and diminishes storage redundancy, as well as inherits the dynamic labeling ability from original scheme. Performance is further improved by introducing some optimization techniques. Our extensive experimental results show that prime number labeling scheme for DAG outperforms interval-based and prefix-based labeling schemes in most cases.