Knowledge Transfer between Computer Vision and Text Mining

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

Autorentext
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.

Inhalt

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.