Learning image manifolds by semantic subspace projection

In many image retrieval applications, the mapping between highlevel 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 highdimensional space and find that multiple subspaces may correspond to one semantic concept. By intelligently utilizing the similarity and dissimilarity information in semantic and geometric (image) domains, we find an optimal Semantic Subspace Projection (SSP) that captures the most important properties of the subspaces with respect to classification. Theoretical analysis proves that the well-known Linear Discriminant Analysis (LDA) could be formulated as a special case of our proposed method. To capture th...

• Type: Conference
• Year: 2006
• Where: MM
• Authors: Jie Yu, Qi Tian
• Keywords: MM 2006, Semantic, Semantic Concept, Semantic Subspace Projection