A Compositional Model for Low-Dimensional Image Set Representation
Author(s)Mobahi, Hossein; Liu, Ce; Freeman, William T.
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Learning a low-dimensional representation of images is useful for various applications in graphics and computer vision. Existing solutions either require manually specified landmarks for corresponding points in the images, or are restricted to specific objects or shape deformations. This paper alleviates these limitations by imposing a specific model for generating images, the nested composition of color, shape, and appearance. We show that each component can be approximated by a low-dimensional subspace when the others are factored out. Our formulation allows for efficient learning and experiments show encouraging results.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition
Institute of Electrical and Electronics Engineers (IEEE)
Mobahi, Hossein, Ce Liu, and William T. Freeman. “A Compositional Model for Low-Dimensional Image Set Representation.” 2014 IEEE Conference on Computer Vision and Pattern Recognition (June 2014).
Author's final manuscript