Abstract
Creating a visual codebook is an important problem in object recognition. Using a compact visual codebook can boost computational efficiency and reduce memory cost. A simple and effective method is proposed for visual feature codebook construction. On the basis of a feedforward hierarchical model, a robust local descriptor is proposed and an a priori statistical scheme is applied to the class-specific feature-learning stage. The experiments show that the proposed approach achieves reliable performance with shorter codebook length, and incremental learning can be easily enabled.
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.
