In this paper, a model for multi-level image annotation that is performed in two phases is proposed. In the first phase, a Naïve Bayes classifier is used to classify low-level image features into elementary classes. In the second phase, a knowledge representation scheme based on Fuzzy Petri Net is used to expand the level of […]
Laboratory for Computer Vision, Virtual and Augmented reality
A knowledge-based multi-layered image annotation system
Highlights A fuzzy-knowledge based intelligent system for multilayered image annotation Novel merged statistical and knowledge-based approach for image interpretation Automatic acquisition of facts and rules about the concepts, and their reliability. Inconsistency checking of image segments classification. Automatic knowledge-based scene recognition and inference of more abstract classes.
