This paper compares the performance of four statistical classifiers, namely linear discriminant classifier, quadratic discriminant classifier, k-Nearest Neighborhood classifier, and parzen classifier are considered for recognition of 2D-shapes. The two features from morphological skeletons and four features from morphological shape decomposition are identified from 2D-shapes. These features are...

In this paper we propose a face recognition algorithm that combines internal and external information of face images. Most of the previous works dealing with face recognition use only internal face features to classify, not considering the information located at head, chin and ears. Here we propose an adaptation of a top-down segmentation algorithm to extract external features from face images,...

In this work, new acoustic features for continuous speech recognition based on the short-term Fourier phase spectrum are introduced for mono (telephone) recordings. The new phase based features were combined with standard Mel Frequency Cepstral Coefficients (MFCC), and results were produced with and without using additional linear discriminant analysis (LDA) to choose the most relevant features...

purpose: to assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. materials and methods: in this study a combination of power spectral density and a series of statistical features are proposed as statistical-frequency features. next, a feature selection method from pattern recognition (pr) tools is presented to extra...

Global features-based methods and local features –based methods have been very successful in face recognition system, yet they can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both descriptor for face images with Local Multiple Pattern, and discriminant learning techniques with Exponential Discriminant Analysis. A combinati...

This paper addresses the problem of Near Duplicate document. Propose a new method to detect near duplicate document from a large collection of document set. This method is classified into three steps. Feature selection, similarity measures and discriminant function. Feature selection performs pre-processing; calculate the weight of each terms and heavily weighted term is selected as a features ...

Feature extraction plays an essential role in high-dimensional data classification. Linear discriminant analysis (LDA) is one of the most well-known methods for reducing data dimensionality in various fields. However, there are three inherent limitations when applying LDA to extract features. First, the number of features that can be extracted by LDA is the number of classes minus one at most. ...

We consider the supervised classification setting, in which the data consist of p features measured on n observations, each of which belongs to one of K classes. Linear discriminant analysis (LDA) is a classical method for this problem. However, in the high-dimensional setting where p ≫ n, LDA is not appropriate for two reasons. First, the standard estimate for the within-class covariance matri...