Active learning with support vector machine(SVM) selects most informative unlabeled images for user labeling,
however small training samples affect its performance. To improve active learning and use more unlabeled data, we
propose a new algorithm training three SVMs separately on the color, texture and shape features of labeled images with
three different kernel functions according to the features' distinct statistical properties. Different algorithms are used in
the selection of disagreement and agreement samples from unlabeled data and also in the calculation of their confidence
degrees. The lowest confident disagreement samples are returned to user to label and added to the training data set with
the highest confident agreement samples. Experimental results verify the high effectiveness of our method in image
retrieval.
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.