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Knowledge transfer between computer ...

Ionescu, Radu Tudor.

Knowledge transfer between computer vision and text miningsimilarity-based learning approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Knowledge transfer between computer vision and text miningby Radu Tudor Ionescu, Marius Popescu.
Reminder of title:	similarity-based learning approaches /
Author:	Ionescu, Radu Tudor.
other author:	Popescu, Marius.
Published:	Cham :Springer International Publishing :2016.
Description:	xxiv, 250 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-30367-3
ISBN:	9783319303673$q(electronic bk.)

Knowledge transfer between computer vision and text miningsimilarity-based learning approaches /
Ionescu, Radu Tudor.

Knowledge transfer between computer vision and text miningsimilarity-based learning approaches /[electronic resource] :by Radu Tudor Ionescu, Marius Popescu. - Cham :Springer International Publishing :2016. - xxiv, 250 p. :ill. (some col.), digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

Motivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.

This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning techniques founded on this approach. Topics and features: Describes a variety of similarity-based learning approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms Presents a nearest neighbor model based on a novel dissimilarity for images, and applies this for handwritten digit recognition and texture analysis Discusses a novel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition and text categorization by topic Introduces an approach based on string kernels for native language identification Contains links for downloading relevant open source code With a foreword by Prof. Florentina Hristea This unique work will be of great benefit to researchers, postgraduate and advanced undergraduate students involved in machine learning, data science, text mining and computer vision. 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.

ISBN: 9783319303673$q(electronic bk.)

Standard No.: 10.1007/978-3-319-30367-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

Knowledge transfer between computer vision and text miningsimilarity-based learning approaches /
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505 0 $a Motivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.
520 $a This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning techniques founded on this approach. Topics and features: Describes a variety of similarity-based learning approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms Presents a nearest neighbor model based on a novel dissimilarity for images, and applies this for handwritten digit recognition and texture analysis Discusses a novel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition and text categorization by topic Introduces an approach based on string kernels for native language identification Contains links for downloading relevant open source code With a foreword by Prof. Florentina Hristea This unique work will be of great benefit to researchers, postgraduate and advanced undergraduate students involved in machine learning, data science, text mining and computer vision. 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.
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