Visual information retrieval systems use low-level such as color, texture and shape for queries. Users usually have a more abstract notion of what will satisfy them. Using low-level to correspond to high-level abstractions is one aspect of the gap.In this paper, we introduce intermediate features. These are low-level semantic features and 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 can then be used in an retrieval system.We report experiments where intermediate 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.