Discriminative methods such as SVM, have been validated extremely efficient in pattern recognition issues. We present a systematic study on smile detection with different SVM classifiers. We experimented with linear SVM classifier, RBF kernel SVM classifier and a recentlyproposed local linear SVM (LL-SVM) classifier. In this paper, we focus on smile detection in face images captured in real-wor...

Zoning is widely used for obtaining topological features of handwritten characters. In this paper we have modified the zoning criteria using fuzzy boundary of individual zones keeping the point in mind that writing style of different persons may shift the structural pattern of the character from one zone to the adjacent zone. The zone-wise normalized distance of individual black pixel along wit...

A kernel orthogonal subspace projection (KOSP) algorithm has been developed for nonlinear approximating subpixel proportion in this paper. The algorithm applies linear regressive model to the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared-error regressor. The algorithm includes two steps: the first step is to select the feat...

Support Vector Machines (SVMs) are investigated for visual gender classification with low-resolution “thumbnail” faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such a...

Nonlinear Support Vector Machines (SVMs) are investigated for appearance-based gender classification with low resolution “thumbnail” faces processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such...

This study employed a hybrid system for the combination of pixel-based (PB) and object-oriented (OO) Support Vector Machines (SVMs) based on Bayesian Probability Theory (BPT) for improved land cover classification. A set of uncorrelated feature attributes have been generated from a one-meter IKONOS satellite image. Four different SVMs kernels were compared and tested to classify buildings, tree...

BACKGROUND AND PURPOSE Estimates of blood volume and volume transfer constant are parameters commonly used to characterize hemodynamic properties of brain lesions. The purposes of this study were to compare values of volume transfer constant and estimates of blood volume in high-grade gliomas on a pixel-by-pixel basis to comprehend whether they provide different information and to compare estim...

Support Vector Machines (SVMs) are investigated for visual gender classification with low resolution “thumbnail” faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such a...

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable top...