Radu Tudor Ionescu, Marius Popescu

Knowledge Transfer between Computer Vision and Text Mining

Similarity-based Learning Approaches

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Advances in Computer Vision and Pattern Recognition
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-80791-1
• Softcover reprint of the original 1st ed. 2016
• Seitenzahl: 276
• Erscheinungstermin: 27. Mai 2018
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 473g
• ISBN-13: 9783319807911
• ISBN-10: 3319807919
• Artikelnr.: 53577504

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.

---

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.