Title :
A Compositional Model for Low-Dimensional Image Set Representation
Author :
Mobahi, Hossein ; Ce Liu ; Freeman, William T.
Author_Institution :
MIT Cambridge, Cambridge, MA, USA
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.
Keywords :
computer vision; image colour analysis; image representation; learning (artificial intelligence); shape recognition; compositional model; computer vision; image color; image shape; low-dimensional image set representation; manifold learning; Image color analysis; Manifolds; Optical imaging; Optimization; Shape; Transforms; Vectors;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
DOI :
10.1109/CVPR.2014.172
