Elements of information theory
Elements of information theory
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Role of Independence for Visual Recognition?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning to Recognize 3D Objects with SNoW
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Journal of Cognitive Neuroscience
International Journal of Computer Mathematics - Bioinformatics
Optimized Associative Memories for Feature Selection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Minimum Bayes error features for visual recognition
Image and Vision Computing
Feature selection by nonparametric Bayes error minimization
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Understanding TSP difficulty by learning from evolved instances
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
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
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Discovering the suitability of optimisation algorithms by learning from evolved instances
Annals of Mathematics and Artificial Intelligence
Phase based 3d texture features
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Feature selection for retrieval purposes
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Multistage face recognition using adaptive feature selection and classification
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Classification of digital photos taken by photographers or home users
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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We have recently shown that 1) the infomax principle for the organization of perceptual systems leads to visual recognition architectures that are nearly optimal in the minimum Bayes error sense, and 2) a quantity which plays an important role in infomax solutions is the marginal diversity(MD): the average distance between the classconditional density of each feature and their mean. Since MD is a discriminant quantity and can be computed with great efficiency, the principle of maximum marginal diversity (MMD) was suggested for discriminant feature selection. In this paper, we study the optimality (in the infomax sense) of the MMD principle and analyze its effectiveness for feature selection in the context of visual recognition. In particular, 1) we derive a close form relation between the optimal infomax and MMD solutions, and 2) show that there is a family of classification problems for which the two are identical. Examination of this family in light of recent studies on the statistics of natural images suggests that the equivalence conditions are likely to hold for the problem of visual recognition. We present experimental evidence supporting the conclusions that 1) MD is a good predictor for the recognition ability of a given set of features, 2) MMD produces features that are more discriminant than those obtained with currently predominant criteria such as energy compaction, and 3) the extracted features are detectors of visual attributes that are perceptually relevant for low-level image classification.