High-Level Concept Detection Based on Mid-Level Semantic Information and Contextual Adaptation

In this paper we propose the use of enhanced mid-level information, such as information obtained from the application of supervised or unsupervised learning methodologies on low-level characteristics, in order to improve semantic multimedia analysis. High-level, a priori contextual knowledge about the semantic meaning of objects and their low-level visual descriptions are combined in an integrated approach that handles in a uniform way the gap between semantics and low-level features. Prior work on low-level feature extraction is extended and a region thesaurus containing all mid-level features is constructed using a hierarchical clustering method. A model vector that contains the distances from each mid-level element is formed and a neural network-based detector is trained for each semantic concept. Contextual adaptation improves the quality of the produced results, by utilizing fuzzy algebra, fuzzy sets and relations. The novelty of the presented work is the context- driven mid-level manipulation of region types, utilizing a domain-independent ontology infrastructure to handle the knowledge. Early experimental results are presented using data derived from the beach domain.