This paper presents an adaptive visual learning algorithm for object tracking. We formulate a novel discriminative generative framework that generalizes the conventional Fisher Linear Discriminant algorithm with a generative model and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the tar...

In this paper, we propose a new consonant recogmtIOn method which integrates two stochastic method: discriminant analysis and HMM (Hidden Markov Models). Discriminant Analysis is effective to analyze local patterns around the reference-point of a consonant such as a burst point. This method, however, is based on the assumption that the reference-point is detected precisely. HMM is able to extra...

This paper presents an adaptive discriminative generative model that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update t...

This work discusses the improvements which can be expected when applying linear feature-space transformations based on Linear Discriminant Analysis (LDA) within automatic speechrecognition (ASR). It is shown that different factors influence the effectiveness of LDA-transformations. Most importantly, increasing the number of LDA-classes by using time-aligned states of Hidden-Markov-Models instea...

In this work, we compare the performance of three modern speaker verification systems and non-expert human listeners in the presence of voice mimicry. Our goal is to gain insights on how vulnerable speaker verification systems are to mimicry attack and compare it to the performance of human listeners. We study both traditional Gaussian mixture model-universal background model (GMM-UBM) and an i...

This paper proposes a statistic framework for segmenting textured areas over real images by discriminant snakes. Our active contour model has the ability to learn different texture prototypes and generate a global statistical model from a multi-valued function. This function is generated by means of filter responses over the texture regions. Linear discriminant analysis is performed to obtain a...

In recent years, a considerable amount of work has been devoted to generalizing linear discriminant analysis to overcome its incompetence for high-dimensional classification (Witten & Tibshirani 2011, Cai & Liu 2011, Mai et al. 2012, Fan et al. 2012). In this paper, we develop high-dimensional semiparametric sparse discriminant analysis (HD-SeSDA) that generalizes the normal-theory discriminant...

As the vital component of a recently developed stochastic model based feature generation scheme, Fisher score is increasingly used in classification applications. In this work we present a generalization of previous proposed feature generation schemes by introducing the concept of multi-class mapping which is oriented to multi-class classification problems. Based on the generalized feature gene...

Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes a new approach to detecting hidden messages in images. The approach uses a wavelet-like decomposition to build higherorder statistical models of natural images. A Fish...

Semi-supervised speaker clustering refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. In the form of an independent training set, the prior knowledge helps us learn a speaker-discriminative feature transformation, a universal speaker prior model, and a discriminative speaker subspace, or equivalently a speaker-discriminative di...