Methods for combining experts' probability assessments
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Image Segmentation for Multimedia Applications
Journal of Intelligent and Robotic Systems
Statistical color models with application to skin detection
International Journal of Computer Vision
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Convex Optimization
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Amsterdam Library of Object Images
International Journal of Computer Vision
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
CCIW'11 Proceedings of the Third international conference on Computational color imaging
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Opponent colors for human detection
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Pedestrian detection in images via cascaded L1-norm minimization learning method
Pattern Recognition
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Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can 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 computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods.