Editing Boosting (machine learning) (section)

===Status quo for object categorization===
The recognition of object categories in images is a challenging problem in [[computer vision]], especially when the number of categories is large.  This is due to high intra class variability and the need for generalization across variations of objects within the same category. Objects within one category may look quite different. Even the same object may appear unalike under different viewpoint, [[Scaling (geometry)|scale]], and [[Illumination (image)|illumination]]. Background clutter and partial occlusion add difficulties to recognition as well.<ref>A. Opelt, A. Pinz, et al., "Generic Object Recognition with Boosting", IEEE Transactions on PAMI 2006</ref>  Humans are able to recognize thousands of object types, whereas most of the existing [[object recognition]] systems are trained to recognize only a few,{{How many|date=October 2018}} e.g. [[Face|human faces]], [[car]]s, simple objects, etc.<ref>M. Marszalek, "Semantic Hierarchies for Visual Object Recognition", 2007</ref>{{Update inline|date=October 2018|reason=The source is from 2007 and computer vision is a lot more advanced now|?=yes}}  Research has been very active on dealing with more categories and enabling incremental additions of new categories, and although the general problem remains unsolved, several multi-category objects detectors (for up to hundreds or thousands of categories<ref>{{Cite web|url=http://image-net.org/challenges/LSVRC/2017/|title=Large Scale Visual Recognition Challenge|date=December 2017}}</ref>) have been developed.  One means is by [[Feature (computer vision)|feature]] sharing and boosting.