Learning image manifolds by semantic subspace projection

In many image retrieval applications, the mapping between high-level semantic concept and low-level features is obtained through a learning process. Traditional approaches often assume that images with same semantic label share strong visual similarities and should be clustered together to facilitate modeling and classification. Our research indicates this assumption is inappropriate in many cases. Instead we model the images as lying on non-linear image subspaces embedded in the high-dimensional space and find that multiple subspaces may correspond to one semantic concept.

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