A feature vector is a representation of an object using a set of numerical or symbolic characteristics, known as features. These vectors are crucial in machine learning and pattern recognition, as they provide a mathematical and easily analyzable way to describe objects.

Key Principles

A feature vector is essentially a series of numbers, similar to a matrix with one column but multiple rows. Each element in the vector represents a specific feature of the object. For instance, in the case of an RGB color description, a feature vector would be [R, G, B], where R, G, and B represent the intensities of red, green, and blue, respectively.

Uses of Feature Vectors

Feature vectors are widely used in various fields due to their effectiveness in representing objects numerically. This numerical representation allows for various types of analyses and comparisons. For example, in image processing, features can include gradient magnitude, color, grayscale intensity, edges, and areas. These features can be compared numerically once they are represented as feature vectors.