Departmental Papers (CIS)

Date of this Version

2011

Document Type

Conference Paper

Comments

Zhang, W., Srinivasan, P., Shi, J. IEEE Conference on Computer Vision and Pattern Recognition. 2011.

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Digital Object Identifier : 10.1109/CVPR.2011.5995342

Abstract

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.

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Date Posted: 16 July 2012