Fragmentation in the Vision of Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Generative/Discriminative Learning Algorithm for Image Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Emotion related structures in large image databases
Proceedings of the ACM International Conference on Image and Video Retrieval
Color Constancy Using Natural Image Statistics and Scene Semantics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisher information and the combination of RGB channels
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
The Weibull manifold in low-level image processing: An application to automatic image focusing
Image and Vision Computing
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We use the theory of group representations to construct very fast image descriptors that split the vector space of local RGB distributions into small group-invariant subspaces. These descriptors are group theoretical generalizations of the Fourier Transform and can be computed with algorithms similar to the FFT. Because of their computational efficiency they are especially suitable for retrieval, recognition and classification in very large image datasets. We also show that the statistical properties of these descriptors are governed by the principles of the Extreme Value Theory (EVT). This enables us to work directly with parametric probability distribution models, which offer a much lower dimensionality and higher resolution and flexibility than histogram representations. We explore the connection to EVT and analyse the characteristics of these descriptors from a probabilistic viewpoint with the help of large image databases.