Structured Regression for Efficient Object Detection
Christoph Lampert
Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
A crucial step on the road towards automatic image
interpretation is the identification and localization of
objects in a scene, i.e. Object Detection. Most current
methods for this task are modifications of Object
Classification techniques, based e.g. on a sliding
window procedure. This has two drawbacks: it is unclear
how to best train a classifier for this task, and the large
number of possible subwindows makes the problem
computationally expensive.
I will explain how recent developments in machine learning
allow us to overcome both problems by treating object
localization as a regression task into a structured output
domain. Using a maximum-margin formulation, we derive
an algorithm that is trained and evaluated as a generalized
support vector machine. This allows robust and efficient
object detection for arbitrary object categories.