Knowledge Transfer between Computer Vision and Text Mining: Similarity-based Learning Approaches

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.