The Design of Images Database by Using Bootstrapping

Abstract

In this paper, we have designed a database that can automatically classify images; for the purpose of sorting through a large number of images more conveniently and thus save manpower and resources. This database is characterized by high level features to image classifying. Its features include: extending a keyword through bootstrapping construction. Whereas common ways of extending a keyword deal with its definition, bootstrapping construction allows expansion through associative extension. This type of keyword expansion mechanism is capable of classifying images in ways that WordNet cannot. Aside from using bootstrapping construction to expand keywords and to classify images, we have also added a discriminative feature metric to increase the precision and recall rates of image classifying to our standards. Through the use of bootstrapping construction, the user could greatly increase the precision and accuracy of grouping images while constructing the image database.