15165209
A PCA-Based Vehicle Classification Framework
Due to its great practical importance, Intelligent Transportation System has been an active research area in recent years. In this paper, we present a framework that incorporates various aspects of an intelligent transportation system with its ultimate goal being vehicle classification. Given a traffic video sequence, the proposed system first proceeds to segment individual vehicles. Then the extracted vehicle objects are normalized so that all vehicles are aligned along the same direction and measured at the same scale. Following the preprocessing step, two classification algorithms - Eigenvehicle and PCA-SVM, are proposed and implemented to classify vehicle objects into trucks, passenger cars, vans, and pick-ups. These two methods exploit the distinguishing power of Principal Component Analysis (PCA) at different granularities with different learning mechanisms. Experiments are conducted to compare these two methods and the results demonstrate the effectiveness of the proposed framework.
14274462
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Algorithm based clustering method to reduce the search space. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm is demonstrated in comparative studies.
12010325
An information-based sequence distance and its application to whole mitochondrial genome phylogeny
MOTIVATION
Traditional sequence distances require an alignment and therefore are not directly applicable to the problem of whole genome phylogeny where events such as rearrangements make full length alignments impossible. We present a sequence distance that works on unaligned sequences using the information theoretical concept of Kolmogorov complexity and a program to estimate this distance.


RESULTS
We establish the mathematical foundations of our distance and illustrate its use by constructing a phylogeny of the Eutherian orders using complete unaligned mitochondrial genomes. This phylogeny is consistent with the commonly accepted one for the Eutherians. A second, larger mammalian dataset is also analyzed, yielding a phylogeny generally consistent with the commonly accepted one for the mammals.


AVAILABILITY
The program to estimate our sequence distance, is available at http://www.cs.cityu.edu.hk/~cssamk/gencomp/GenCompress1.htm. The distance matrices used to generate our phylogenies are available at http://www.math.uwaterloo.ca/~mli/distance.html.
1552680
Shared information and program plagiarism detection
A fundamental question in information theory and in computer science is how to measure similarity or the amount of shared information between two sequences. We have proposed a metric, based on Kolmogorov complexity, to answer this question and have proven it to be universal. We apply this metric in measuring the amount of shared information between two computer programs, to enable plagiarism detection. We have designed and implemented a practical system SID (Software Integrity Diagnosis system) that approximates this metric by a heuristic compression algorithm. Experimental results demonstrate that SID has clear advantages over other plagiarism detection systems. SID system server is online at http://software.bioinformatics.uwaterloo.ca/SID/.
18290114
Improved Rates and Asymptotic Normality for Nonparametric Neural Network Estimators
We obtain an improved approximation rate (in Sobolev norm) of r/sup -1/2-/spl alpha//(d+1)/ for a large class of single hidden layer feedforward artificial neural networks (ANN) with r hidden units and possibly nonsigmoid activation functions when the target function satisfies certain smoothness conditions. Here, d is the dimension of the domain of the target function, and /spl alpha//spl isin/(0, 1) is related to the smoothness of the activation function. When applying this class of ANNs to nonparametrically estimate (train) a general target function using the method of sieves, we obtain new root-mean-square convergence rates of Op([n/log(n)]/sup -/(1+2/spl alpha//(d+1))/[4(1+/spl alpha//(d+1))])=op(n/sup -1/4/) by letting the number of hidden units /spl tau//sub n/, increase appropriately with the sample size (number of training examples) n. These rates are valid for i.i.d. data as well as for uniform mixing and absolutely regular (/spl beta/-mixing) stationary time series data. In addition, the rates are fast enough to deliver root-n asymptotic normality for plug-in estimates of smooth functionals using general ANN sieve estimators. As interesting applications to nonlinear time series, we establish rates for ANN sieve estimators of four different multivariate target functions: a conditional mean, a conditional quantile, a joint density, and a conditional density. We also obtain root-n asymptotic normality results for semiparametric model coefficient and average derivative estimators.
960732
Computing the Assignment of Orthologous Genes via Genome Rearrangement
The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not clearly delineate the evolutionary relationship among genes of the same families. In this paper, we present a new approach to ortholog assignment that takes into account both sequence similarity and evolutionary events at genome level, where orthologous genes are assumed to correspond to each other in the most parsimonious evolving scenario under genome rearrangement. It is then formulated as a problem of computing the signed reversal distance with duplicates between two genomes of interest, for which an efcient heuristic algorithm was given by introducing two new optimization problems, minimum common partition and maximum cycle decomposition. Following this approach, we have implemented a high-throughput system for assigning orthologs on a genome scale, called SOAR, and tested it on both simulated data and real genome sequence data. Compared to a recent ortholog assignment method based entirely on homology search (called INPARANOID), SOAR shows a marginally better performance in terms of sensitivity on the real data set because it was able to identify several correct orthologous pairs that were missed by INPARANOID. The simulation results demonstrate that SOAR in general performs better than the iterated exemplar algorithm in terms of computing the reversal distance and assigning correct orthologs.
13960729
Multimodal user input patterns in a non-visual context
How will users choose between speech and hand inputs to perform tasks when they are given equivalent choices between both modalities in a non-visual interface? This exploratory study investigates this question. The study was conducted using AudioBrowser, a non-visual information access for the visually impaired. Findings include: (1) Users chose between input modalities based on the type of operations undertaken. Navigation operations primarily used hand input on the touchpad, while non-navigation instructions primarily used speech input. (2) Surprisingly, multimodal error correction was not prevalent. Repeating a failed operation until it succeeded and trying other methods in the same input modality were dominant error-correction strategies. (3) The modality learned first was not necessarily the primary modality used later, but a training order effect existed. These empirical results provide implications for designing non-visual multimodal input dialogues.