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Proceedings of the ACM workshop on 3D object retrieval
High-dimensional spectral feature selection for 3D object recognition based on reeb graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
An unsupervised approach to feature discretization and selection
Pattern Recognition
Efficient feature selection filters for high-dimensional data
Pattern Recognition Letters
Boosting k-NN for Categorization of Natural Scenes
International Journal of Computer Vision
Heat flow-thermodynamic depth complexity in directed networks
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Information-theoretic selection of high-dimensional spectral features for structural recognition
Computer Vision and Image Understanding
How do image complexity, task demands and looking biases influence human gaze behavior?
Pattern Recognition Letters
Classification of sign-based image representations based on distance functions
Pattern Recognition and Image Analysis
Information-Theoretic dissimilarities for graphs
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Information theory has proved to be effective for solving many computer visionand pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information), principles (maximum entropy, minimax entropy) and theories (rate distortion theory, method of types). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.