Learning Spatial Context: Using Stuff to Find Things

The sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the object. Other types of objects of amorphous spatial extent (e.g., trees, sky), however, are more naturally classified based on texture or color. In this paper, we seek to combine recognition of these two types of objects into a system that leverages "context" toward improving detection. In particular, we cluster image regions based on their ability to serve as context for the detection of objects. Rather than providing an explicit training set with region labels, our method automatically groups regions based on both their appearance and their relationships to the detections in the image. We show that our things and stuff (TAS) context model produces meaningful clusters that are readily interpretable, and helps improve our detection ability over state-of-the-art detectors. We also present a method f...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Geremy Heitz, Daphne Koller
• Keywords: Computer Vision, Detection Ability, ECCV 2008, Object Detection, Overhead Car Detection, State-of-the-art Detectors, VOC 2005/2006 Datasets