Abstract
We propose a method for semantic categorization and retrieval of photographic images based on low-level image descriptors derived from perceptual experiments. The method applies multidimensional scaling and hierarchical clustering analysis to identify candidate semantic categories into which human observers organize images. Through a series of subjective experiments we refine our definition of these categories and select a set of low-level image features that uniquely describe them. We then devise a new image similarity metric and develop a prototype system, which identifies the semantic category of the image and retrieves similar images from the database. We tested the metric on a set of new images and compared the categorization results with that of human observers. Our results provide a good match to human performance, thus validating the use of human judgments to develop semantic descriptors. Our method can be used for the enhancement of current retrieval methods, better organization of image/video databases, and the development of more intuitive navigation schemes, browsing methods and user interfaces.
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.