17504688
A statistical method for binary classification of images
The classification of documents with sparse text, and video analysis, relies on accurate image classification. We herein present a method for binary classification that accommodates any number of individual classifiers. Each individual classifier is defined by the critical point between its two means, and its relative weighting is inversely proportional to its expected error rate. Using 10 simple image analysis metrics, we distinguish a set of "natural" and "city" scenes, providing a "semantically meaningful" classification. The optimal combination of 5 of these 10 classifiers provides 85.8% accuracy on a small (120 image) feasibility corpus. When this feasibility corpus is then split into half training and half testing images, the mean accuracy of the optimum set of classifiers was 81.7%. Accuracy as high as 90% was obtained for the test set when training percentage was increased. These results demonstrate that an accurate classifier can be constructed from a large pool of simple classifiers through the use of the statistical ("Normal") classification method described herein.
15275424
Blur identification based on kurtosis minimization
In this paper, we describe an algorithm for identifying a parametrically described blur based on kurtosis minimization. Using different choices for the parameters of the blur, the noisy blurred image is restored using Wiener filter. We use the kurtosis as a measurement of the quality of the restored image. From the set of the candidate deblurred images, the one with the minimum kurtosis is selected. The proposed technique is tested in a simulated experiment on a variety of blurs including atmospheric turbulence blurs, Gaussian blurs, and out-of-focus blurs. The proposed approach is also tested on real blurred images. Moreover, we test the performance when a wrong blur model is given. Our experiments show that the kurtosis minimization measurements match well with methods that maximize PSNR.
17329223
Supereulerian planar graphs
We investigate the supereulerian graph problems within planar graphs, and we prove that if a 2-edge-connected planar graph G is at most three edges short of having two edge-disjoint spanning trees, then G is supereulerian except a few classes of graphs. This is applied to show the existence of spanning Eulerian subgraphs in planar graphs with small edge cut conditions. We also determine several extremal bounds for planar graphs to be supereulerian.
17075751
A collaboration specification language
COCA (Collaborative Objects Coordination Architecture) was proposed as a novel means to model and support collaborations over the Internet. Our approach separates coordination policies from user interfaces and the policies are specified in a logic-based language. Over the past year, both the collaboration model and the specification language have been substantially refined and evaluated through our experience in building real-life collaboration systems. This paper presents the design of the specification language and illustrates the main ideas with a few simple design examples. Semantics, implementation, runtime support, and applications are also covered but not as the focus of this paper.
5115871
Temporal Knowledge Discovery with Infrequent Episodes
In this paper, we present an efficient algorithm which discovers rare episodes with a combination of bottom-up and top-down scanning schema. The information sharing between bottom-up and top-down scannings helps prune candidate episodes, and thus, efficiently find infrequent episodes that are interesting to user: We evaluate the performance of the algorithm using real-life weather databases. We observe from experimental results that our approach results in 30%-90% reduction in computation time and 25%-75% reduction in the number of candidates comparing with Apriori algorithm.
27085223
Normal vector identification and interactive tradeoff analysis using minimax formulation in multiobjective optimization
In multiobjective optimization, tradeoff analysis plays an important role in determining the best search direction to reach a most preferred solution. This paper presents a new explicit interactive tradeoff analysis method based on the identification of normal vectors on a noninferior frontier. The interactive process is implemented using a weighted minimax formulation by regulating the relative weights of objectives in a systematic manner. It is proved under a mild condition that a normal vector can be identified using the weights and Kuhn-Tucker (K-T) multipliers in the minimax formulation. Utility gradients can be estimated using local preference information such as marginal rates of substitution. The projection of a utility gradient onto a tangent plane of the noninferior frontier provides a descent direction of disutility and thereby a desirable tradeoff direction, along which tradeoff step sizes can be decided by the decision maker using an explicit tradeoff table. Necessary optimality conditions are established in terms of normal vectors and utility gradients, which can be used to guide the elicitation of local preferences and also to terminate an interactive process in a rigorous yet flexible way. This method is applicable to both linear and nonlinear (either convex or nonconvex) multiobjective optimization problems. Numerical examples are provided to illustrate the theoretical results of the paper and the implementation of the proposed interactive decision analysis process.
6337639
Transparent sharing and interoperation of heterogeneous single-user applications
Multi-user applications generally lag behind in features or compatibility with single-user applications. As a result, users are often not motivated to abandon their favorite single-user applications for groupware features that are less frequently used. A well-accepted approach, collaboration transparency, is able to convert off-the-shelf single-user applications into groupware without modifying the source code. However, existing systems have been largely striving to develop generic application-sharing mechanisms and undesirably force users to share the same application in cooperative work. In this paper we analyze this problem and present a novel approach (called intelligent collaboration transparency) to addressing this problem. Our approach allows for heterogeneous application sharing by considering the particular semantics of the applications and the collaboration task in question.