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In this paper, we present a novel approach to multi-view gender classification considering both shape and texture information to represent facial image. The face area is divided into small regions, from which local binary pattern(LBP) histograms are extracted and concate-nated into a single vector efficiently representing the facial image. The(More)
In this paper, we propose a new method for decomposing pattern classification problems based on the class relations among training data. By using this method, we can divide a K-class classification problem into a series of ((2)(K)) two-class problems. These two-class problems are to discriminate class Ci from class Cj for i=1, ..., K and j = i+1, while the(More)
Chinese word segmentation is an active area in Chinese language processing though it is suffering from the argument about what precisely is a word in Chinese. Based on corpus-based segmentation standard, we launched this study. In detail, we regard Chinese word segmentation as a character-based tagging problem. We show that there has been a potent trend of(More)
In this paper, we use EEG signals to classify two emotions-happiness and sadness. These emotions are evoked by showing subjects pictures of smile and cry facial expressions. We propose a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered. We use common spatial patterns (CSP) and linear-SVM to classify(More)
This paper is concerned with Chinese word segmentation, which is regarded as a character based tagging problem under conditional random field framework. It is different in our method that we consider both feature template selection and tag set selection, instead of feature template focused only method in existing work. Thus, there comes an empirical(More)
In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea(More)
Spectral clustering is an elegant and powerful approach for clustering. However, the underlying eigen-decomposition takes cubic time and quadratic space w.r.t. the data set size. These can be reduced by the Nyström method which samples only a subset of columns from the matrix. However, the manipulation and storage of these sampled columns can still be(More)
Electroencephalogram (EEG) is the most commonly studied signal for vigilance estimation. Up to now, many researches mainly focus on using supervised learning methods for analyzing EEG data. However, it is hard to obtain enough labeled EEG data to cover the whole vigilance states, and sometimes the labeled EEG data may be not reliable in practice. In this(More)
Visual dictionary learning and base (binary) classifier training are two basic problems for the recently most popular image cate-gorization framework, which is based on the bag-of-visual-terms (BOV) models and multiclass SVM classifiers. In this paper, we study new algorithms to improve performance of this framework from these two aspects. Typically SVM(More)