2480556
Data Brokers: Building Collections through Automated Negotiation
Collecting digital materials is time-consuming and can gain from automation. Since each source – and even each acquisition – may involve a separate negotiation of terms, a collector may prefer to use a broker to represent his interests with owners. This paper describes the Data Broker Framework (DBF), which is designed to automate the process of digital object acquisition. For each acquisition, a negotiation agent is assigned to negotiate on the collector’s behalf, choosing from strategies in a strategy pool to automatically handle most bargaining cases and decide what to accept and what counteroffers to propose. We introduce NOODLE (Negotiation OntOlogy Description LanguagE) to formally specify terms in the negotiation domain.
9589989
Efficient photometric stereo technique for three-dimensional surfaces with unknown BRDF
The present paper focuses on efficient inverse rendering using a photometric stereo technique for realistic surfaces. The technique primarily assumes the Lambertian reflection model only. For non-Lambertian surfaces, application of the technique to real surfaces in order to estimate 3D shape and spatially varying reflectance from sparse images remains difficult. In the present paper, we propose a new photometric stereo technique by which to efficiently recover a full surface model, starting from a small set of photographs. The proposed technique allows diffuse albedo to vary arbitrarily over surfaces while non-diffuse characteristics remain constant for a material. Specifically, the basic approach is to first recover the specular reflectance parameters of the surfaces by a novel optimization procedure. These parameters are then used to estimate the diffuse reflectance and surface normal for each point. As a result, a lighting-independent model of the geometry and reflectance properties of the surface is established using the proposed method, which can be used to re-render the images under novel lighting via traditional rendering methods.
13746374
A Generalized Output-Coding Scheme with SVM for Multiclass Microarray Classification
Multiclass cancer classification based on microarray data is described. A generalized output-coding scheme combined with binary classifiers is used. Different coding strategies, decoding functions and feature selection methods are combined and validated on two cancer datasets: GCM and ALL. The effects of these different methods and their combinations are then discussed. The highest testing accuracies achieved are 78% and 100% for the two datasets respectively. The results are considered to be very good when compared with the other researchers’ work.
14935665
Optimal Matrix Computing Using Vector Division with Sub-word Parallel
Available computing is expected to be effective and efficient in ubiquitous multimedia services. And, matrix multiplication is particular to ubiquitous computing such as image transformation so that it is urgent to exploit potential parallelism as well as data reuse in matrix multiplication. Therefore, based on synchronized data triggered architecture (SDTA), we propose a method of parallel vector division (PVD) as well as sub-word parallel (SWP) supported on SDTA to optimize the matrix computation on specified architecture. Experimental results show that PVD with SWP is suitable for matrix multiplication on SDTA instruction set architecture. Besides, we could extend the proposed model into other data-intensive computing on SDTA.
31075540
Reducing multiclass cancer classification to binary by output coding and SVM
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.
62074016
Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data
The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.
61766535
Photometric stereo and weather estimation using internet images
We extend photometric stereo to make it work with internet images, which are typically associated with different viewpoints and significant noise. For popular tourism sites, thousands of images can be obtained from internet search engines. With these images, our method computes the global illumination for each image and the surface orientation at some scene points. The illumination information can then be used to estimate the weather conditions (such as sunny or cloudy) for each image, since there is a strong correlation between weather and scene illumination. We demonstrate our method on several challenging examples.