Similarity-Based Pattern Analysis and Recognition

✍ Scribed by Marcello Pelillo (auth.), Marcello Pelillo (eds.)

• Publisher: Springer-Verlag London
• Year: 2013
• Tongue: English
• Leaves: 293
• Series: Advances in Computer Vision and Pattern Recognition
• Edition: 1
• Category: Library

✦ Synopsis

This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms; reviews similarity measures for non-vectorial data, considering both a “kernel tailoring” approach and a strategy for learning similarities directly from training data; describes various methods for “structure-preserving” embeddings of structured data; formulates classical pattern recognition problems from a purely game-theoretic perspective; examines two large-scale biomedical imaging applications.

✦ Table of Contents

Front Matter....Pages I-XIV
Introduction: The SIMBAD Project....Pages 1-10
Front Matter....Pages 11-11
Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness....Pages 13-44
SIMBAD: Emergence of Pattern Similarity....Pages 45-64
Front Matter....Pages 65-65
On the Combination of Information-Theoretic Kernels with Generative Embeddings....Pages 67-83
Learning Similarities from Examples Under the Evidence Accumulation Clustering Paradigm....Pages 85-117
Front Matter....Pages 119-119
Geometricity and Embedding....Pages 121-155
Structure Preserving Embedding of Dissimilarity Data....Pages 157-177
A Game-Theoretic Approach to Pairwise Clustering and Matching....Pages 179-216
Front Matter....Pages 217-217
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma....Pages 219-245
Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness....Pages 247-287
Back Matter....Pages 289-291

✦ Subjects

Pattern Recognition