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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, (eingeladen).

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.

Visual Information Retrieval

http://www.ims.tuwien.ac.at/~hme/