Peekaboo

There is apparently a "new" trend in computer vision that I seem to have missed. It's explicit feature maps . So what is this about? We all know and love the kernel trick: have some vector representation X of your data and some algorithm (like SVMs) that rely only on dot products in the input space. Replace the dot products by some positive definite kernel and by Mercer's Theorem there is some Hilbert space of functions Y and some mapping phi: X -> Y such that your kernelized algorithm works as if you mapped your input to phi. This extremely powerfull method is now used everywhere, most of all for SVMs. But it has several significant drawbacks in this case: Training using a non-linear kernel is a lot slower [O(n^3)] than training a linear SVM [O(n)]. Kernel SVMs take a lot of memory to store, since they are "non-parametric" in a sense. In addition to the Lagrange-Multipliers alpha, all the support vectors have to be stored. Recall is painfully...

I was just reading the paper in the title ( pdf ), which Alexei Efros talked about at CVML. It's from this years ICCV. The basic idea of the paper is to do object detection by training a linear SVM on hog features for each positive example. I thought the idea was pretty cool and wanted to write something about it... and then I saw there is already a post by the first author, Tomasz Malisiewicz, in his own blog . He also discusses some of his (matlab :( ) code for non-maximum suppression. So check out his blog for more details on this cool paper :)

Some tips and facts that I took from the summer school. They are pretty random but may be usefull for pactitioners of vision. Many may seem obvious - but I just didn't see it before ... Gist doesn't work on cropped or rotated images. Since it does a kind of whole image template matching, this is pretty clear. And maybe it shouldn't - the scene layout is changed after all. For doing BoW Cordelia Schmid suggests (and I guess uses) 6x6 patches and a pyramid with scale factor 1.2. Scaling is done by Gaussian convolution. Ponce uses 10x10 patches to do sparse coding. If you combine multiple features using MKL or by just adding up kernels (which is the same as concatenating features), normalize each feature by it's variance and then search for a joint gamma. This heuristic get's you out of doing grid search over a huge space! Cordelia Schmid thinks that "clever clusters" don't help much in doing BoW. She thinks it's more important to work ...