Knowledge Transfer between Computer Vision and Text Mining: Similarity-based Learning Approaches: Advances in Computer Vision and Pattern Recognition
Autor Radu Tudor Ionescu, Marius Popescuen Limba Engleză Hardback – 9 mai 2016
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Specificații
ISBN-13: 9783319303659
ISBN-10: 3319303651
Pagini: 342
Ilustrații: XXIV, 250 p. 42 illus., 33 illus. in color.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.67 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Computer Vision and Pattern Recognition
Locul publicării:Cham, Switzerland
ISBN-10: 3319303651
Pagini: 342
Ilustrații: XXIV, 250 p. 42 illus., 33 illus. in color.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.67 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Computer Vision and Pattern Recognition
Locul publicării:Cham, Switzerland
Cuprins
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.
Notă biografică
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.
Textul de pe ultima copertă
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:
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.
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 asseveral 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
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.
Caracteristici
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