Knowledge Transfer between Computer Vision and Text Mining / Advances in Computer Vision and Pattern Recognition (PDF)

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 techniques founded on this
approach.

Topics and features:

• Describes a
  variety of similarity-based learning approaches, including nearest neighbor
  models, local learning, kernel methods, and clustering algorithms
• Presents a
  nearest neighbor model based on a novel dissimilarity for images, and applies
  this for handwritten digit recognition and texture analysis
• Discusses a
  novel kernel for (visual) word histograms, as well asseveral kernels based on pyramid representation, and uses these for facial expression recognition and
  text categorization by topic
• Introduces an
  approach based on string kernels for native language identification
• Contains links
  for downloading relevant open source code
• With a foreword
  by Prof. Florentina Hristea

This unique work will be of great benefit to
researchers, postgraduate and advanced undergraduate students involved in
machine learning, data science, text mining and computer vision.

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.