Abstract
Image annotation can be formulated as a multi-class classification problem. A multi-class classification problem can be solved by ensemble classifiers. We investigate the ensemble of multiple two-class classifiers based on MPEG-7 standard. To get ride of redundancy information, a binary-coded chromosome genetic algorithm is used to select individual optimal classification feature pattern for each two-class other than for all the classes. Two-class classifiers are generated based on the results of feature selection, and majority voting scheme is used to combine two-class classifiers. The experimental results over 2000 classified Corel images show that our approaches can improve annotation accuracies.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
