We propose a similarity-based learning style algorithm by regarding each image as a multi-instance (MI) sample for
image classification. An image featured as vectorial representation interesting regions is transferred to a MI sample.
Then a similarity like matrix is constructed using MI kernel between given images and some carefully selected base
images, as the new representation of given images. Three selection strategies are proposed to build the base images set to
find an optimal solution. A Weka implementation decision tree is used as the main learner in this paper. Experiments on
image data repository ALOI and Corel Image 2000 show the effectiveness of the proposed algorithm compared to some
previous based line methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.