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In this paper, we propose a novel method, called local non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose lo-calization constraint, in addition to the non-negativity constraint in the standard NMF [1]. This gives a set of bases which(More)
An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view(More)
In this paper, we propose an invariant signature representation for appearances of 3-D object under varying view and illumination, and a method for learning the signature from multi-view appearance examples. The signature, a nonlinear feature, provides a good basis for 3-D object detection and pose estimation due to its following properties: (1) Its(More)
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