6hpdqwlff)hdwxuhh/d\huvvlqq&rqwhqwedvhgg,pdjhh5hwulhydo ,psohphqwdwlrqqrii+xpdqq:ruogg)hdwxuhvv

The major problem of most CBIR approaches is bad quality in terms of recall and precision. As a major reason for this, the semantic gap between high-level concepts and low-level features has been identified. In this paper we describe an approach to reduce the impact of the semantic gap by deriving high-level (semantic) from low-level features and using these features to improve the quality of CBIR queries. This concept is implemented for a high-level feature class that describes human world properties and evaluated in 300 queries. Results show that using those high-level features improves the quality of result sets by balancing recall and precision.