44494763
Inter-Frame Interpolation By Snake Model And Greedy Algorithm
In this paper, we present a novel method to solve the inter-frame interpolation problem in image morphing. We use our improved snake model that is associated with the gravitational force field to locate control points in the object contours. Afterwards, we apply the greedy algorithm in free-form deformations to achieve optimal warps among feature point pairs in starting and ending frames. The new method uses an energy-minimization function under the influence of inter-frames. The energy serves to impose frame-wise and curve-wise constraints among the interpolated frames.
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.
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.
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.
11423295
Multi-view face identification and pose estimation using B-spline interpolation
The available face views in the training set are mostly limited. In this paper, we present a view interpolation method using nonlinear B-spline on face manifolds. Two models, the inner-outer ellipse model and the moment of inertia model, are developed to estimate the pose orientation. We use the limited view-pose face images to form the pose eigen space. Then, based on these nonlinear manifolds we form a B-spline for each individual. Identification is to compute the shortest Euclidean distance from a given test view to the nearest point within one of these B-splines. Once the test view is classified as a familiar individual in the training set, not only can the individual be identified, but also the pose angle can be estimated. Experimental results show that B-spline interpolation can achieve a recognition rate of 95%.
9235487
Queuing Analysis on MIMO Systems with Adaptive Modulation and Coding
The combined MIMO with adaptive modulation and coding (AMC) technology can provide high spectral efficiency and link robustness. While most existing adaptive algorithms focus on physical layer, cross-layer analysis on the queuing behavior of MIMO-AMC systems is necessary, but remains open. In this paper, under the conditions of unsaturated traffic and finite-length buffer, we investigate the queuing characters of two representative categories of MIMO systems, namely the BLAST system and the space-time block coding (STBC) system. We model the service processes of both STBC and BLAST coupled with AMC, which is the most challenging part of the queuing analysis. We observe a new tradeoff between diversity and multiplexing in terms of link layer packet loss rate and queuing delay, based on which we propose a cross-layer design of diversity-multiplexing switching scheme to optimize the QoS of the MIMO-AMC systems.
11313782
Efficient Heuristics for Solving Probabilistic Interval Algebra Networks
A probabilistic interval algebra (PIA) network is an interval algebra network with probabilities associated with the labels on an edge. The probabilities on each edge sum to 1. A solution is a consistent scenario where the product of the probabilities associated with each unique edge label is maximized. In this paper we investigate previous PIA network solution algorithms, and propose new ones. Our first algorithm is based on best first search and guarantees to output the optimal solution. However, this algorithm is only feasible for toy problems. We augment the algorithm with three heuristics. Although our proposed algorithm does not guarantee an optimal solution, it is very useful in practice. Good solutions can be generated quickly
38683441
A Wordnet-Based Approach to Feature Selection in Text Categorization
This paper proposes a new feature selection method for text categorization. In this method, word tendency, which takes related words into consideration, is used to select best terms. Our experiments on binary classification tasks show that our method achieves better than DF and IG when the classes are semantically discriminative. Furthermore, our best performance is usually achieved in fewer features.
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.
10126460
Mining common topics from multiple asynchronous text streams
Text streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a large volume of data. An effective way to explore the semantic as well as temporal information in text streams is topic mining, which can further facilitate other knowledge discovery procedures. In many applications, we are facing multiple text streams which are related to each other and share common topics. The correlation among these streams can provide more meaningful and comprehensive clues for topic mining than those from each individual stream. However, it is nontrivial to explore the correlation with the existence of asynchronism among multiple streams, i.e. documents from different streams about the same topic may have different timestamps, which remains unsolved in the context of topic mining. In this paper, we formally address this problem and put forward a novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step extracts common topics from multiple streams based on the adjusted timestamps by the second step; the second step adjusts the timestamps of the documents according to the time distribution of the discovered topics by the first step. We perform these two steps alternately and a monotone convergence of our objective function is guaranteed. The effectiveness and advantage of our approach were justified by extensive empirical studies on two real data sets consisting of six research paper streams and two news article streams, respectively.