10274461
Pattern Classification Using Rectified Nearest Feature Line Segment
This paper proposes a new classification method termed Rectified Nearest Feature Line Segment (RNFLS). It overcomes the drawbacks of the original Nearest Feature Line (NFL) classifier and possesses a novel property that centralizes the probability density of the initial sample distribution, which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex problems such as two-spirals, which the original NFL cannot deal with properly. Experimental comparisons with NFL, NN(Nearest Neighbor), k-NN and NNL (Nearest Neighbor Line) using artificial and real-world datasets demonstrate that RNFLS offers the best performance.
205904713
Fuzzy aggregated connectedness for image segmentation
Fuzzy methodology has been successfully applied to image analysis and pattern recognition since the theory of fuzzy set was 0rst introduced by Zadeh [1]. It is believed that the fuzzy set theory models the inherent imprecision in images. Fuzzy geometrical approaches focus on the spatial measures and have attracted considerable research interest in handling the spatial uncertainty in images [2]. Fuzzy connectedness is an important topological property of images. The classical de0nition was given by Rosenfeld [3]. A modi0cation to the traditional concept of fuzzy connectedness was further proposed [4]. Based on the notion of fuzzy connectedness of image elements, fuzzy objects for n-dimensional digital spaces was exploited for image segmentation [5]. Fuzzy connectedness is a natural extension to the crisp connectedness and re:ects the imprecise topological structures of images. The algorithms in the literature usually compute the fuzzy connectedness with regard to the best path between a pair of points. This approach su;ers from high sensitivity to noise. Noise in corrupted images may misguide the detection of the best path and further in:uence the determination of fuzzy connectedness. We believe that this has mainly been due to the binarization in determining the best path. The information of a path is either fully preserved or discarded and this contradicts the concept of fuzzy sets. In this paper,
16801553
Mini-Max Initialization for Function Approximation
No abstract available.
37744500
Improving Nearest Neighbor Classification with Simulated Gravitational Collapse
The performance of the Nearest Neighbor classifier drops significantly with the increase of the overlapping of the distribution of different classes. To overcome this drawback, we propose to simulate the physical process of gravitational collapse to trim the boundaries of the distribution of each class to reduce overlapping. The proposed simulated gravitational collapse(SGC) algorithm is tested on 7 real-world data sets. Experimental results show that the nearest prototype classifier based on SGC outperforms conventional NN and k-NN classifiers.
26820236
3-D IFS fractals as real-time graphics model
Abstract Fractals have recently been attracting increasing research interest. Unlike conventional geometrical figures, fractals have unlimited details—a potentially useful feature for artistic images. One way to generate fractals is to use Iterated Function Systems (IFS). There have been significant research efforts on 2-D IFS. Programs for rendering 2-D IFS fractals are readily available. 3-D IFS appear to be a natural extension but remain an area to be investigated. This paper explores the use of 3-D IFS for creating fascinating fractal scenes. An efficient coloring, lighting and mist effects scheme is proposed. A program using the proposed scheme has been developed that can render delicate scenes consisting of multiple IFS fractals in real-time on the PC.
8311095
Classifying image texture with statistical landscape features
This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.
14613142
Coupled Bayesian Framework for Dual Energy Image Registration
Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.
14759214
Real-coded ECGA for economic dispatch
In this paper, we propose a new approach that consists of the extended compact genetic algorithm (ECGA) and split-on-demand (SoD), an adaptive discretization technique, to economic dispatch (ED) problems with nonsmooth cost functions. ECGA is designed for handling problems with decision variables of the discrete type, while the decision variables of ED problems are oftentimes real numbers. Thus, in order to employ ECGA to tackle ED problems, SoD is utilized for discretizing the continuous decision variables and works as the interface between ECGA and the ED problem. Furthermore, ED problems in practice are usually hard for traditional mathematical programming methodologies because of the equality and inequality constraints. Hence, in addition to integrating ECGA and SoD, in this study, we devise a repair operator specifically for making the infeasible solutions to satisfy the equality constraint. To examine the performance and effectiveness, we apply the proposed framework to two different-sized ED problems with nonsmooth cost function considering the valve-point effects. The experimental results are compared to those obtained by various evolutionary algorithms and demonstrate that handling ED problems with the proposed framework is a promising research direction.
8136301
Architecture classification for SOA-based applications
The architecture of SOA-based applications is different from traditional software architecture where the architecture is mainly static. The architecture of an SOA-based application is dynamic, i.e., the application may be composed at runtime using existing services. Thus SOA has provided a new direction for software architecture study, where the architecture is determined at runtime and architecture can be dynamically changed at runtime to meet the new software requirements. This paper proposes an architecture classification scheme for SOA-based applications. Using this classification, several well-known SOA-based applications are reviewed including the architectures proposed and adopted by major computer companies and standard organizations. The architecture classification provides a unified way to evaluate a variety of architectures for SOA-based applications
36509694
Collaborative Internet Worm Containment
Large-scale worm outbreaks that lead to distributed denial-of-service attacks pose a major threat to Internet infrastructure security. Fast worm containment is crucial for minimizing damage and preventing flooding attacks against network hosts.
14744012
Winner-update algorithm for nearest neighbor search
This paper presents an algorithm, called the winner-update algorithm, for accelerating the nearest neighbor search. By constructing a hierarchical structure for each feature point in the l/sub p/ metric space, this algorithm can save a large amount of computation at the expense of moderate preprocessing and twice the memory storage. Given a query point, the cost for computing the distances from this point to all the sample points can be reduced by using a lower bound list of the distance established from Minkowski's inequality. Our experiments have shown that the proposed algorithm can save a large amount of computation, especially when the distance between the query point and its nearest neighbor is relatively small. With slight modification, the winner-update algorithm can also speed up the search for k nearest neighbors, neighbors within a specified distance threshold, and neighbors close to the nearest neighbor.