CBIR with a subspace tree: principal component analysis versus averaging
The subspace tree is an indexing method for large multi-media databases. The search in such a tree starts at the subspace with the lowest dimension. In this subspace, the set of all possible similar images is determined. In the next subspace, additional metric information corresponding to a higher dimension is used to reduce this set. We compare theoretically and empirically data-dependent mappings into subspaces (principal component analysis) with data-independent mapping (averaging). The empirical experiments are performed on an image collection of 30,000 images.
cbir subspace tree high-dimensional indexing image pyramid pca