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.
28737704
Feature-Correlation Based Multi-view Detection
A view validation algorithm has been shown to predict whether or not the views are sufficiently compatible for solving a particular learning task. But it only works when a natural split of features exists. If the split does not exist, it will fail to manufacture a feature split to build the best views. In this paper, we present a general algorithm CCFP (Correlation and Compatibility based Feature Partitioner) to automate multi-view detection. CCFP first labels the large amount of unlabeled examples using single view algorithm, then calculates the conditional SU (Symmetric Uncertainty) between every pair of features and the IG (Information Gain) of each feature given the examples labeled previously by single view algorithm with high-confidence predictions. According to the estimated values of SU and IG, all the features will be partitioned into two views that are low correlated, compatible and sufficient enough. The experiment results show that multi-view learner with views generated by CCFP outperforms learner with views generated by other means clearly.
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.
26966022
Term Committee Based Event Identification within News Topics
Most previous research focus on organizing news set into flat collections of stories. However, a topic in news is more than a mere collection of stories: it is characterized by a definite structure of inter-related events. Stories within a topic usually share some terms which are related to the topic other than a specific event, so stories of different events are usually very similar to each other within a topic. To deal with this problem, we propose a new event identification method based on the term committee. We first capture some tight term clusters as term committees of potential events, and then use them to reweight the key terms in a story. The experimental results on two Linguistic Data Consortium (LDC) datasets show that the proposed method for event identification outperforms previous methods significantly.
18767470
New event detection based on indexing-tree and named entity
New Event Detection (NED) aims at detecting from one or multiple streams of news stories that which one is reported on a new event (i.e. not reported previously). With the overwhelming volume of news available today, there is an increasing need for a NED system which is able to detect new events more efficiently and accurately. In this paper we propose a new NED model to speed up the NED task by using news indexing-tree dynamically. Moreover, based on the observation that terms of different types have different effects for NED task, two term reweighting approaches are proposed to improve NED accuracy. In the first approach, we propose to adjust term weights dynamically based on previous story clusters and in the second approach, we propose to employ statistics on training data to learn the named entity reweighting model for each class of stories. Experimental results on two Linguistic Data Consortium (LDC) datasets TDT2 and TDT3 show that the proposed model can improve both efficiency and accuracy of NED task significantly, compared to the baseline system and other existing systems.
5878411
Density-Weighted Nystrm Method for Computing Large Kernel Eigensystems
The Nystrm method is a well-known sampling-based technique for approximating the eigensystem of large kernel matrices. However, the chosen samples in the Nystrm method are all assumed to be of equal importance, which deviates from the integral equation that defines the kernel eigenfunctions. Motivated by this observation, we extend the Nystrm method to a more general, density-weighted version. We show that by introducing the probability density function as a natural weighting scheme, the approximation of the eigensystem can be greatly improved. An efficient algorithm is proposed to enforce such weighting in practice, which has the same complexity as the original Nystrm method and hence is notably cheaper than several other alternatives. Experiments on kernel principal component analysis, spectral clustering, and image segmentation demonstrate the encouraging performance of our algorithm.