Color Histogram Classification using NMF
David Guillamet, Bernt Schiele · 2001
Introduction Visual recognition of objects is one of the most challenging problems in computer vision and artificial intelligence. Approaches to solve this problem have focused on using several methodologies of which, the most common is Principal Component Analysis (PCA). PCA was initially used to describe face patterns in a lower-dimensional space than the image space [18]. Other approaches have also focused on this technique to overcome frequent computer vision problems such as the recognition of objects taken under a wide range of conditions (several viewpoints and illumination conditions) [12], or dealing with partial occlusions by using robust estimation techniques [1, 5]. However, PCA based techniques suffer from several difficulties. Mainly, an image projection to a PCA based space depends on the precise position of relevant objects, on the intensity and shape of background zones, and on intensity and color of illumination. Since PCA treats its inputs globally, the relevant ob