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Knowledge Transfer between Computer Vision and Text Mining

  • Livre Relié
  • 250 Nombre de pages
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This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string proces... Lire la suite
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Description

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 (SBL) techniques founded on this approach. Topics and features: describes a variety of SBL approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms; presents a nearest neighbor model based on a novel dissimilarity for images; discusses a novel kernel for (visual) word histograms, as well as several kernels based on a pyramid representation; introduces an approach based on string kernels for native language identification; contains links for downloading relevant open source code.

Provides a novel perspective on image analysis and text processing, presenting the scientific justification for treating the two disciplines in a similar manner

Offers open source code for the techniques in the book at an associated website

Reviews state-of-the-art similarity-based learning approaches, including nearest neighbor models, kernel methods and clustering algorithms



Auteur
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.


Contenu

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

Informations sur le produit

Titre: Knowledge Transfer between Computer Vision and Text Mining
Auteur:
Code EAN: 9783319303659
ISBN: 978-3-319-30365-9
Format: Livre Relié
Editeur: Springer, Berlin
Genre: Informatique
nombre de pages: 250
Poids: 598g
Taille: H18mm x B244mm x T164mm
Année: 2016
Auflage: 1st ed. 2016

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