Visual information retrieval systems use low-level features such as color, texture and shape for image queries. Users usually have a more abstract notion of what will satisfy them. Using low-level features to correspond to high-level abstractions is one aspect of the semantic gap.In this paper, we introduce intermediate features. These are low-level "semantic features" and "high level image" features. That is, in one hand, they can be arranged to produce high level concept and in another hand, they can be learned from a small annotated database. These features can then be used in an image retrieval system.We report experiments where intermediate features are textures. These are learned from a small annotated database. The resulting indexing procedure is then demonstrated to be superior to a standard color histrogram indexing.
['Mohamad Obeid', 'Bruno Jedynak', 'Mohamed Daoudi']
Image indexing & retrieval using intermediate features