A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Anisotropic diffusion of multivalued images with applications to color filtering
IEEE Transactions on Image Processing
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
A new framework for feature descriptor based on SIFT
Pattern Recognition Letters
Color face recognition for degraded face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning Photometric Invariance for Object Detection
International Journal of Computer Vision
Original paper: Real time feature extraction and Standard Cutting Models fitting in grape leaves
Computers and Electronics in Agriculture
New colour SIFT descriptors for image classification with applications to biometrics
International Journal of Biometrics
Universal seed skin segmentation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
ICSR'10 Proceedings of the Second international conference on Social robotics
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Novel color Gabor-LBP-PHOG (GLP) descriptors for object and scene image classification
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Gabor-Based novel local, shape and color features for image classification
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Facial image medical analysis system using quantitative chromatic feature
Expert Systems with Applications: An International Journal
An improvement to the SIFT descriptor for image representation and matching
Pattern Recognition Letters
Target detection based on a dynamic subspace
Pattern Recognition
DeepCAPTCHA: an image CAPTCHA based on depth perception
Proceedings of the 5th ACM Multimedia Systems Conference
New color GPHOG descriptors for object and scene image classification
Machine Vision and Applications
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model induces the equivalence classes to the actual algorithms. As there are many color models available, the inherent difficulty is how to automatically select a single color model or, alternatively, a weighted subset of color models producing the best result for a particular task. The subsequent hurdle is how to obtain a proper fusion scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color model selection and fusion of feature detection algorithms, in this paper, we propose a method that exploits nonperfect correlation between color models or feature detection algorithms derived from the principles of diversification. As a consequence, a proper balance is obtained between repeatability and distinctiveness. The result is a weighting scheme which yields maximal feature discrimination. The method is verified experimentally for three different image feature detectors. The experimental results show that the fusion method provides feature detection results having a higher discriminative power than the standard weighting scheme. Further, it is experimentally shown that the color model selection scheme provides a proper balance between color invariance (repeatability) and discriminative power (distinctiveness).