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Type(s) de contenu et mode(s) de consultation : Texte : électronique

Titre(s) : Similarity-based pattern analysis and recognition [Texte électronique] / Marcello Pelillo, editor

Publication : London ; New York : Springer, cop. 2013

Description matérielle : 1 online resource (xiv, 291 pages)

Collection : Advances in computer vision and pattern recognition

Note(s) : Includes bibliographical references and index
. - Online resource; title from PDF title page (ebrary, viewed December 30, 2013).
The pattern recognition and machine learning communities have, until recently, focused mainly on feature-vector representations, typically considering objects in isolation. However, this paradigm is being increasingly challenged by similarity-based approaches, which recognize the importance of relational and similarity information. 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 that provide assistance in the diagnosis of physical and mental illnesses from tissue microarray images and MRI images This pioneering work is essential reading for graduate students and researchers seeking an introduction to this important and diverse subject. Marcello Pelillo is a Full Professor of Computer Science at the University of Venice, Italy. He is a Fellow of the IEEE and of the IAPR

Autre(s) auteur(s) : Pelillo, Marcello. Fonction indéterminée  Voir les notices liées en tant qu'auteur

Sujet(s) : Vision par ordinateur  Voir les notices liées en tant que sujet
Reconnaissance des formes (informatique)  Voir les notices liées en tant que sujet
Identification biométrique  Voir les notices liées en tant que sujet
Reconnaissance de l'activité humaine (informatique)  Voir les notices liées en tant que sujet
Informatique omniprésente  Voir les notices liées en tant que sujet
Informatique  Voir les notices liées en tant que sujet

Indice(s) Dewey : 006.37 (23e éd.)  Voir les notices liées en tant que sujet

Numéros : ISBN 9781447156284

Notice n° :  FRBNF44663662 (notice reprise d'un réservoir extérieur)

Table des matières : Introduction ; Part I: Foundational Issues ; Non-Euclidean Dissimilarities ; SIMBAD ; Part II: Deriving Similarities for Non-vectorial Data ; On the Combination of Information Theoretic Kernels with Generative Embeddings ; Learning Similarities from Examples under the Evidence Accumulation Clustering Paradigm ; Part III: Embedding and Beyond ; Geometricity and Embedding ; Structure Preserving Embedding of Dissimilarity Data ; A Game-Theoretic Approach to Pairwise Clustering and Matching ; Part IV: Applications ; Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma ; Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness.

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