Knowledge Transfer Between Computer Vision and Text Mining: Similarity-based Learning Approaches - Rilegato

Ionescu, Radu Tudor; Popescu, Marius

Rilegato

ISBN 10: 3319303651 ISBN 13: 9783319303659

Casa editrice: Springer-Verlag New York Inc, 2016

Riassunto

This ground-breaking text/reference divergesfrom the traditional view that computer vision (for image analysis) and stringprocessing (for text mining) are separate and unrelated fields of study,propounding that images and text can be treated in a similar manner for thepurposes of information retrieval, extraction and classification. Highlightingthe benefits of knowledge transfer between the two disciplines, the textpresents a range of novel similarity-based learning (SBL) techniques founded onthis approach. Topics and features: describes a variety of SBL approaches,including nearest neighbor models, local learning, kernel methods, andclustering algorithms; presents a nearest neighbor model based on a noveldissimilarity for images; discusses a novel kernel for (visual) wordhistograms, as well as several kernels based on a pyramid representation; introducesan approach based on string kernels for native language identification; containslinks for downloading relevant open source code.

Su questo libro

Informazioni sull?autore

Dr. Radu Tudor Ionescu is an Assistant Professor in the Department of Computer Science at the University of Bucharest, Romania.

Dr. Marius Popescu is an Associate Professor at the same institution.

Dalla quarta di copertina

This ground-breaking text/reference diverges from thetraditional view that computer vision (for image analysis) and stringprocessing (for text mining) are separate and unrelated fields of study,propounding that images and text can be treated in a similar manner for thepurposes of information retrieval, extraction and classification. Highlightingthe benefits of knowledge transfer between the two disciplines, the textpresents a range of novel similarity-based learning techniques founded on thisapproach.

Topics and features:

• Describes avariety of similarity-based learning approaches, including nearest neighbormodels, local learning, kernel methods, and clustering algorithms
• Presents anearest neighbor model based on a novel dissimilarity for images, and appliesthis for handwritten digit recognition and texture analysis
• Discusses anovel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition andtext categorization by topic
• Introduces anapproach based on string kernels for native language identification
• Contains linksfor downloading relevant open source code
• With a forewordby Prof. Florentina Hristea

This unique work will be of great benefit toresearchers, postgraduate and advanced undergraduate students involved inmachine learning, data science, text mining and computer vision.

Dr. Radu Tudor Ionescu is an AssistantProfessor in the Department of Computer Science at the University of Bucharest,Romania. Dr. Marius Popescu is an AssociateProfessor at the same institution.

Dettagli dell'edizione

• Editore: Springer-Verlag New York Inc
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319303651
• ISBN 13: 9783319303659
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 276
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania