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.
H. Eidenberger, C. Breiteneder: "Semantic Feature Layers in Content-based Image Retrieval: Implementation of Human World Features"; in: "International Conference on Control, Automation, Robotics and Computer Vision", Elsevier Computer Science, 2002, (invited).
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