Abstract

Ob ject 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 Dis- criminant Analysis, and show that LDA models can be trained almost trivially, and with little or no loss in performance. The covariance matri- ces we estimate capture properties of natural images. Whitening HOG features with these covariances thus removes naturally occuring correla- tions between the HOG features. We show that these whitened features (which we call WHO) are considerably better than the original HOG fea- tures for computing similarities, and prove their usefulness in clustering. Finally, we use our findings to produce an ob ject detection system that is competitive on PASCAL VOC 2007 while being considerably easier to train and test.