  Linear Support Vector Machines trained on HOG features are now a de facto
standard across many visual perception tasks. Their popularisation can largely
be attributed to the step-change in performance they brought to pedestrian
detection, and their subsequent successes in deformable parts models. This
paper explores the interactions that make the HOG-SVM symbiosis perform so
well. By connecting the feature extraction and learning processes rather than
treating them as disparate plugins, we show that HOG features can be viewed as
doing two things: (i) inducing capacity in, and (ii) adding prior to a linear
SVM trained on pixels. From this perspective, preserving second-order
statistics and locality of interactions are key to good performance. We
demonstrate surprising accuracy on expression recognition and pedestrian
detection tasks, by assuming only the importance of preserving such local
second-order interactions.
