Abstract
In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different approaches. And these features are often separately selected and learned by machine learning methods. In this paper, the relation between distinct features obtained by different feature extraction approaches and that for the same original images were studied by Kernel Canonical Correlation Analysis (KCCA). We apply a Support Vector Machine (SVM) classifier in the learnt semantic space of the combined features and compare against SVM on the raw data and previously published state-of-the-art results. Experiments show that significant improvement is achieved with the SVM in the semantic space in comparison with direct SVM classification on the raw data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.