Learning Contextual Rules for Priming Object Categories in Images

In this paper we introduce and exploit the concept of contextual rules in the field of object detection. These rules are defined as associations between different object likelihood maps and are learned from given examples. The contextual rules can be used to prime regions where a target object category occurs in an image given areas of other object categories. The principal idea is to locate several basic object categories in an image and then use this information to infer object likelihood maps for other object categories. The proposed framework itself is general and not limited to specific object categories. For demonstrating our approach, we use likely occurrences of pedestrians and windows in urban scenes, extracted by a technique employing visual context, and use them to prime for shop logos.