Discriminative Decorrelation for Clustering and Classification
Object detection has over the past few years converged on
using linear SVMs over HOG features. Training linear SVMs however is
quite expensive, and can become intractable as the number of categories
increase. In this work we revisit a much older technique, viz. Linear
Discriminant Analysis, and show that LDA models can be trained almost
trivially, and with little or no loss in performance. The covariance
matrices we estimate capture properties of natural images. Whitening
HOG features with these covariances thus removes naturally occuring
correlations between the HOG features. We show that these whitened
features (which we call WHO) are considerably better than the original
HOG features for computing similarities, and prove their usefulness in
clustering. Finally, we use our findings to produce an object detection
system that is competitive on PASCAL VOC 2007 while being considerably
easier to train and test.
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Text Reference
Bharath Hariharan, Jitendra Malik, and Deva Ramanan. Discriminative decorrelation for clustering and classification. In ECCV (4), 459–472. 2012.BibTeX Reference
@inproceedings{HariharanMR_ECCV_2012,author = "Hariharan, Bharath and Malik, Jitendra and Ramanan, Deva",
title = "Discriminative Decorrelation for Clustering and Classification",
booktitle = "ECCV (4)",
year = "2012",
pages = "459-472"
}