We propose in this paper a very fast feature selection technique based on conditional mutual information.

boundary surfaces are presented. Independence of shading and classification calculations insures an undistorted visualization of 3-D shape. Non-binary classification operators insure that small or poorly defined features are not IosL The resulting colors and opacities am composited from back to front along

the convergence the more exact the approximation. • If the hidden nodes are binary, then thresholding the loopy beliefs is guaranteed to give the most probable assignment, even though the numerical value of the beliefs may be incorrect. This result only holds for nodes in the loop. In the max-product (or "

of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization

allows for very fast feature extraction. 1

existing feature selection methods with respect to efficiency and effectiveness. In this work, we intro-duce a novel concept, predominant correla-tion, and propose a fast filter method which can identify relevant features as well as re-dundancy among relevant features without pairwise correlation analysis

Algorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while lters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, lters have

We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using

This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different

We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using Ada