5809817
Efficient Kernel Machines Using the Improved Fast Gauss Transform
The computation and memory required for kernel machines with N training samples is at least O(N2). Such a complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N). We also give an error bound for the approximation, and provide experimental results on the UCI datasets.
10638554
Near-optimal regularization parameters for applications in computer vision
Computer vision requires the solution of many ill-posed problems such as optical flow, structure from motion, shape from shading, surface reconstruction, image restoration and edge detection. Regularization is a popular method to solve ill-posed problems, in which the solution is sought by minimization of a sum of two weighted terms, one measuring the error arising from the ill-posed model, the other indicating the distance between the solution and some class of solutions chosen on the basis of prior knowledge (smoothness, or other prior information). One of important issues in regularization is choosing optimal weight (or regularization parameter). Existing methods for choosing regularization parameters either require the prior information on noise in the data, or are heuristic graphical methods. We apply a method for choosing near-optimal regularization parameters by approximately minimizing the distance between the true solution and the family of regularized solutions. We demonstrate the effectiveness of this approach for the regularization on two examples: edge detection and image restoration.
12121845
Automatic image orientation detection
We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.
7211602
Improved fast gauss transform and efficient kernel density estimation
Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.
1160125
Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval
In this paper we propose an approach to deal with the Chinese-English cross-language image retrieval problem. Text-based image retrieval and query translation methods were adopted in the experiments. A similarity-based backward transliteration model with candidate filter was proposed to translate proper nouns that have no entries in a bilingual dictionary. The experimental results showed that using similarity-based backward transliteration increased retrieval performances.
44571521
Designing computing platform for BioGrid
Grid environment allows sharing of a large amount of idle resources of compute and other available information. Accordingly, we have set up the BioGrid for computing resources by combining grid technology with parallel bioinformatics applicable software in erecting the BioGrid computing platform. In one instance for example, biology data which is heterogeneous and grows rapidly, we plan to integrate and share it using grid technology. In this paper, the FASTA applicable software is used to test and analyse the grid environment to reduce time and improve efficacy. The experimental results and performances of the bioinformatics tool use on the grid system are also presented in this paper.
15408418
CatRelate: A New Hierarchical Document Category Integration Algorithm by Learning Category Relationships
We address the problem of integrating documents from a source catalog into a master catalog. Current technologies for solving the problem deem it as a flat category integration problem without considering the useful hierarchy information in the catalog, or deal with it hierarchically but without a rigorous model. In contrast, our method is based on correctly identifying relationships among categories, such as Match, Disjoint, SubConcept, SuperConcept, and Overlap, which come from the relations of sets in Set theory. Compared with traditional Match/NotMatch relationship in literature, our approach is more expressive in defining the relationship. The relationships among categories are first learned in a probabilistic way, and then refined by considering the hierarchy context. Our preliminary experiments show that it can help to correctly identify category relationships, and thus increase the accuracy of document integration.
39460490
Localization Techniques for Cluster-Based Data Grid
In this paper, we present an efficient method for optimizing localities of data distribution when executing data parallel applications. The data to logical grid nodes mapping technique is employed to enhance the performance of parallel programs on cluster grid. Cluster grid is a typical computational grid environment consists of several clusters located in multiple campuses that are distributed globally over the Internet. Objective of the proposed technique is to reduce inter-cluster communication overheads and to speed the execution of data parallel programs in the underlying distributed cluster grid. The theoretical analysis and experimental results show improvement of communication costs and scalable of the proposed techniques on different hierarchical cluster grids.
13803760
A bandwidth-based polling scheme for QoS support in Bluetooth
A flexible bandwidth management scheme namely Bandwidth-based Polling (BBP) for Bluetooth is proposed. A framing structure of time is defined in BBP, and the master allocates proper number of slots for each active slave in a frame. BBP allows the master to poll a slave more than once to achieve high flexibility for bandwidth allocation. The calculation of the polling time as well as the payload type for a slave is according to the bandwidth requirement of the slave and the limit of the frame size controlled by the master. Extension of BBP for supporting slaves with the SCO link and slaves without bandwidth requirement (best-effort slaves) is also proposed. Simulation results have shown the efficiency and fairness of BBP in bandwidth management. The flexibility of bandwidth allocation depends on the maximum polling time preset by BBP. A larger maximum polling time makes the higher flexibility of bandwidth allocation at the expense of a longer latency before reaching the equilibrium state. Analysis of the impact of the maximum polling time on bandwidth allocation is also presented in the paper.