A Compositional Model for Low-Dimensional Image Set Representation

Hossein Mobahi, Ce Liu, William T. Freeman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1322-1329

Abstract


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.

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[bibtex]
@InProceedings{Mobahi_2014_CVPR,
author = {Mobahi, Hossein and Liu, Ce and Freeman, William T.},
title = {A Compositional Model for Low-Dimensional Image Set Representation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}