Abstract
There are many difficulties in cow face recognition, including variable individual poses, challenges in collecting individual data, complex image backgrounds, etc. An improved capsule network (CapsNet) was used to solve these problems. First, we combined convolutional and local binary pattern (LBP) texture features with a feature extractor named C-LBP. Then, by utilizing the self-attention module, the feature extraction capability was enhanced. An intermediate capsule layer was added to improve capsules utilization. We tested our model on datasets of cow faces. According to the experimental data, the proposed model improved the structure of the original CapsNet. In the middle of the training, positional data was added, giving the model a higher performance and greater resilience in cow face recognition.
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.
