Learning the discriminative power-invariance trade-off

DMCA

Learning the discriminative power-invariance trade-off (2007)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Manik Varma , D. Ray

Venue:	IN ICCV
Citations:	227 - 4 self

BibTeX

@INPROCEEDINGS{Varma07learningthe,
    author = {Manik Varma and D. Ray},
    title = {Learning the discriminative power-invariance trade-off},
    booktitle = {IN ICCV},
    year = {2007},
    publisher = {}
}

OpenURL

Abstract

We investigate the problem of learning optimal descriptors for a given classification task. Many hand-crafted descriptors have been proposed in the literature for measuring visual similarity. Looking past initial differences, what really distinguishes one descriptor from another is the tradeoff that it achieves between discriminative power and invariance. Since this trade-off must vary from task to task, no single descriptor can be optimal in all situations. Our focus, in this paper, is on learning the optimal tradeoff for classification given a particular training set and prior constraints. The problem is posed in the kernel learning framework. We learn the optimal, domain-specific kernel as a combination of base kernels corresponding to base features which achieve different levels of trade-off (such as no invariance, rotation invariance, scale invariance, affine invariance, etc.) This leads to a convex optimisation problem with a unique global optimum which can be solved for efficiently. The method is shown to achieve state-of-the-art performance on the UIUC textures, Oxford flowers and Caltech 101 datasets.

Keyphrases

discriminative powerinvariance trade-off scale invariance unique global optimum different level uiuc texture visual similarity particular training set affine invariance past initial difference prior constraint oxford flower optimal descriptor single descriptor kernel learning framework classification task discriminative power base kernel domain-specific kernel convex optimisation problem rotation invariance many hand-crafted descriptor state-of-the-art performance optimal tradeoff

DMCA