Existing salient object extraction methods for the low depth-of-field (DOF) image are usually based on local saliency. However, in the low DOF image, the smooth region of salient objects is similar to the background in local saliency, so they are easily confused. In this paper, a novel salient object extraction method is proposed by introducing Support Vector Data Description (SVDD) for salient object shape description. It is the first time that SVDD is used for salient object extraction. SVDD makes full use of global characteristics of salient objects, which makes it possible for our approach to accurately extract salient objects containing smooth regions. Experiments on a Flickr dataset consisting of 141 low DOF images indicate that F-measure of our approach is better than the existing 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.