Radu Tudor Ionescu, Marius Popescu

Knowledge Transfer between Computer Vision and Text Mining (eBook, PDF)

Similarity-based Learning Approaches

• Format: PDF

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer International Publishing
• Erscheinungstermin: 25. April 2016
• Englisch
• ISBN-13: 9783319303673
• Artikelnr.: 46930449

Autorenporträt

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.

Inhaltsangabe

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.

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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.