Abstract
The purpose of categorizing objects is to identify and locate within an image, instances of an object category. It is a difficult task to identify objects in images when such images contain poor quality, occlusion, background clutter, or noise. It is more difficult when there are too many objects in same scene (background). Most models for categorizing object exploit context information and appearance information from objects in order to enhance recognition accuracy. In the scene, interaction among objects (context information) can successfully help in removing ambiguity of appearance inputs during recognition task. Appearance information can help identify object classes successfully up to some level. The issue of integrating several kinds of contextual information in order to have a robust categorization of object is address in this paper. Various methods of exploiting contextual information for categorizing object are review.