Abstract
In order to fully segment and classify the artistic objectives of painting and realise the needs of automatic classification and retrieval of painting by computer, this paper proposes to establish convolution neural network with dual core compression activation module and deep separation convolution. The DKSE module is constructed based on the structural features of SKNet. SKNet extracts the overall image and detail features, SENet enhances the channel features. Using DKSE module and depth separable convolution, a convolution neural network is established to classify paintings. DKSE module can effectively improve the classification performance of the model, fully extract the overall and local detail features of oil painting images, and provide better classification accuracy than the traditional network model.
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.
