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Knowledge Transfer between Computer Vision and Text Mining

Similarity-based Learning Approaches

  • Book
  • © 2016

Overview

Authors:
  1. Radu Tudor Ionescu

    1. Faculty of Math. and Computer Science, University of Bucharest, Bucharest, Romania

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    You can also search for this author in PubMed   Google Scholar

  2. Marius Popescu

    1. Department of Computer Science, University of Bucharest, Bucharest, Romania

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    You can also search for this author in PubMed   Google Scholar

  • 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

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxiv
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  2. Motivation and Overview

    • Radu Tudor Ionescu, Marius Popescu
    Pages 1-13

  3. Learning based on Similarity

    • Radu Tudor Ionescu, Marius Popescu
    Pages 15-37

  4. Knowledge Transfer from Text Mining to Computer Vision

    1. Front Matter

      Pages 39-39
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    2. State-of-the-Art Approaches for Image Classification

      • Radu Tudor Ionescu, Marius Popescu
      Pages 41-52

    3. Local Displacement Estimation of Image Patches and Textons

      • Radu Tudor Ionescu, Marius Popescu
      Pages 53-98

    4. Object Recognition with the Bag of Visual Words Model

      • Radu Tudor Ionescu, Marius Popescu
      Pages 99-132

  5. Knowledge Transfer from Computer Vision to Text Mining

    1. Front Matter

      Pages 133-133
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    2. State-of-the-Art Approaches for String and Text Analysis

      • Radu Tudor Ionescu, Marius Popescu
      Pages 135-147

    3. Local Rank Distance

      • Radu Tudor Ionescu, Marius Popescu
      Pages 149-191

    4. Native Language Identification with String Kernels

      • Radu Tudor Ionescu, Marius Popescu
      Pages 193-227

    5. Spatial Information in Text Categorization

      • Radu Tudor Ionescu, Marius Popescu
      Pages 229-241

    6. Conclusions

      • Radu Tudor Ionescu, Marius Popescu
      Pages 243-246

  6. Back Matter

    Pages 247-250
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Keywords

About this book

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.

Authors and Affiliations

  • Faculty of Math. and Computer Science, University of Bucharest, Bucharest, Romania

    Radu Tudor Ionescu

  • Department of Computer Science, University of Bucharest, Bucharest, Romania

    Marius Popescu

About the authors

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.

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