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Discriminative Image Warping with Attribute Flow

8 years 5 months ago
Discriminative Image Warping with Attribute Flow
We address the problem of finding deformation between two images for the purpose of recognizing objects. The challenge is that discriminative features are often transformation-variant (e.g. histogram of oriented gradients, texture), while transformation-invariant features (e.g. intensity, color) are often not discriminative. We introduce the concept of attribute flow which explicitly models how image attributes vary with its deformation. We develop a non-parametric method to approximate this using histogram matching, which can be solved efficiently using linear programming. Our method produces dense correspondence between images, and utilizes discriminative, transformation-variant features for simultaneous detection and alignment. Experiments on ETHZ shape categories dataset show that we can accurately recognize highly deformable objects with few training examples.
Weiyu Zhang, Praveen Srinivasan, Jianbo Shi
Added 30 Apr 2011
Updated 30 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Weiyu Zhang, Praveen Srinivasan, Jianbo Shi
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