In this paper, a modified Fisher linear discriminant analysis (FLDA) is proposed and aims to not only overcome the rank limitation of FLDA, that is, at most only finding a discriminant vector for 2-class problem based on Fisher discriminant criterion, but also relax singularity of the within-class scatter matrix and finally improves classification performance of FLDA. Experiments on nine public...

Principal Components Analysis (PCA) is an appearance based technique used widely for the dimensionality reduction and it records a great performance in face recognition. PCA based approaches typically include two phases: training and classification (Draper et al 2003). In the training phase, an Eigen space is established from the training samples using PCA and the training face images are mappe...

We report on an extensive simulation study comparing eight statistical classiication methods, focusing on problems where the number of observations is less than the number of variables. Using a wide range of artiicial and real data, two types of classiiers were contrasted; methods that classify using all variables, and methods that rst reduce the number of dimensions to two or three. The full f...

Extending popular histogram representations of local motion patterns, we present a novel weighted integration method based on an assumption that a motion importance should be changed by its appearance to obtain better recognition accuracies. The proposed integration method of motion and appearance patterns can weight information involving “what is moving” by discriminant way. The discriminant w...

Positive definite kernels, such as Gaussian Radial Basis Functions (GRBF), have been widely used in computer vision for designing feature extraction and classification algorithms. In many cases nonpositive definite (npd) kernels and non metric similarity/dissimilarity measures naturally arise (e.g., Hausdorff distance, Kullback Leibler Divergences and Compact Support (CS) Kernels). Hence, there...

Discriminant analysis plays an important role in statistical pattern recognition. A popular method is the Foley-Sammon optimal discriminant vectors (FSODVs) method, which aims to find an optimal set of discriminant vectors that maximize the Fisher discriminant criterion under the orthogonal constraint. The FSODVs method outperforms the classic Fisher linear discriminant analysis (FLDA) method i...

Automatic processes on seismic data using pattern recognition is one of the interesting fields in geophysical data interpretation. One part is the seismic object detection using different supervised classification methods that finally has an output as a probability cube. Object detection process starts with generating a pickset of two classes labeled as object and non-object and then selecting ...