Abstract
Image classification is a hot topic of pattern recognition in computer vision. In order to achieve high accuracy of classification, a certain amount of high quality pictures are needed. As a matter of fact, high quality pictures are scarce. Active learning can solve such a problem. Label dependences play an important role in multi-label active learning for image classification. The interdependences between different labels are usually different and asymmetrical. This paper first brings the asymmetrical conditional label dependences into a novel active learning method for multi-label image classification based on the asymmetrical conditional label dependence, called ACDAL. Our extensive experimental results on three image and two non-image datasets show that our new approach ACDAL significantly outperforms existing approaches.
Published Version
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.
