A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the goal of sensory coding?
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The psychophysics of texture segmentation
Early vision and beyond
Perception as Bayesian inference
Perception as Bayesian inference
Independent component analysis: algorithms and applications
Neural Networks
Bayesian models for visual information retrieval
Bayesian models for visual information retrieval
Minimum Bayes error features for visual recognition
Image and Vision Computing
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Can feature information interaction help for information fusion in multimedia problems?
Multimedia Tools and Applications
Lightweight probabilistic texture retrieval
IEEE Transactions on Image Processing
Scalable discriminant feature selection for image retrieval and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Independent representations have recently attracted significant attention from the biological vision and cognitive science communities. It has been 1) argued that properties such as sparseness and independence play a major role in visual perception, and 2) shown that imposing such properties on visual representations originates receptive fields similar to those found in human vision. We present a study of the impact of feature independence in the performance of visual recognition architectures. The contributions of this study are of both theoretical and empirical natures, and support two main conclusions. The first is that the intrinsic complexity of the recognition problem (Bayes error) is higher for independent representations. The increase can be significant, close to 10% in the databases we considered. The second is that criteria commonly used in independent component analysis are not sufficient to eliminate all the dependencies that impact recognition. In fact, "independent components" can be less independent than previous representations, such as principal components or wavelet bases.