Subspace indexing for extremely high-dimensional CBIR

We describe a hierarchical linear subspace method to query large on-line image databases using image similarity as the basis of the queries. The method is based on the generic multimedia indexing (GEMINI) approach which is used in the IBM query through the image content search system. Our approach is demonstrated on image indexing, in which the subspaces correspond to different resolutions of the images. During content-based image retrieval, the search starts in the subspace with the lowest resolution of the images. In this subspace, the set of all possible similar images is determined. In the next subspace, additional metric information corresponding to a higher resolution is used to reduce this set. This procedure is repeated until the similar images can be determined. For evaluation we used three image databases and two different subspace sequences.