A Compositional Model for Low-Dimensional Image Set Representation

被引：8

作者：

Mobahi, Hossein ^{[1
]}

Liu, Ce ^{[2
]}

Freeman, William T. ^{[1
]}

机构：

[1] MIT, Cambridge, MA 02139 USA

[2] Microsoft Res, Cambridge, MA USA

来源：

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2014年

关键词：

D O I：

10.1109/CVPR.2014.172

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1322 / 1329

页数：8

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