Learning Photometric Invariance From Diversified Color Model Ensembles

Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (\eg, Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real--world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non--redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative p...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Jose M. Alvarez, Theo Gevers, Antonio M. Lopez
• Keywords: Color Models, Combining Classifiers, Computer Vision, CVPR 2009, Diversified Ensembles, Learning, Object Detection, Photometric Invariance, Road Detection, Shadows, Skin Color Detection