Abstract
We present here, an image description approach based on prosemantic features. These features are obtained through a two-level feature extraction process. A first level of features, related to image structure and color distribution, is extracted from the images, and used as input to a bank of classifiers, each one trained to recognize a given category. Packing together the scores, the features that we call prosemantic are obtained, and used to index images in an image retrieval system where searches are performed using relevance feedback. Prosemantic features have been evaluated on a public domain dataset, and compared against two different sets of features. Our experiments show that the use of prosemantic features allows for a more successful and quick retrieval with respect to the other features considered.
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.
