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 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 algorithmsPresents a
nearest neighbor model based on a novel dissimilarity for images, and applies
this for handwritten digit recognition and texture analysisDiscusses 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 topicIntroduces an
approach based on string kernels for native language identificationContains links
for downloading relevant open source codeWith 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.