Semantic Feature Layers in Content-based Image Retrieval: Implementation of Human World Features

By Horst Eidenberger and Christian Breiteneder

Abstract

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.

Reference

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).

BibTeX

@incollection{TUW-137003,     author = {Eidenberger, Horst and Breiteneder, Christian},     title = {Semantic Feature Layers in Content-based Image Retrieval: Implementation of Human World Features},     booktitle = {International Conference on Control, Automation, Robotics and Computer Vision},     year = {2002},     publisher = {Elsevier Computer Science},     url = {http://www.ims.tuwien.ac.at/~hme/},     keywords = {Visual Information Retrieval},     note = {invited} } Click into the text area and press Ctrl+A/Ctrl+C or ⌘+A/⌘+C to copy the BibTeX into your clipboard… or download the BibTeX.