A Tree-Based Context Model for Object Recognition

—There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. A context model can rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit of context models has been limited because most of the previous methods were tested on data sets with only a few object categories, in which most images contain one or two object categories. In this paper, we introduce a new data set with images that contain many instances of different object categories, and propose an efficient model that captures the contextual information among more than a hundred object categories using a tree structure. Our model incorporates global image features, dependencies between object categories, and outputs of local detectors into one probabilistic framework. We demonstrate that our context model imp...

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Myung Jin Choi, Antonio Torralba, Alan S. Willsky
• Keywords: Object Categories, PAMI 2012, Performance Benefit, Software Engineering, Typical Scenes