Exploiting Hierarchical Context on a Large Database of Object Categories
Author(s)Choi, Myung Jin; Lim, Joseph Jaewhan; Torralba, Antonio; Willsky, Alan S.
MetadataShow full item record
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. Context models can efficiently 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 from using context models has been limited because most of these methods were tested on datasets with only a few object categories, in which most images contain only one or two object categories. In this paper, we introduce a new dataset 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 of object categories. We show that our context model can be applied to scene understanding tasks that local detectors alone cannot solve.
Proceedings of the IEEE Converence on Computer Vision and Pattern Recognition (CVPR), 2010
Institute of Electrical and Electronics Engineers (IEEE)
Willsky, Alan S. et al. "Exploiting hierarchical context on a large database of object categories." Proceedings of the 2010 IEEE Converence on Computer Vision and Pattern Recognition (CVPR): 129-136. © 2010 IEEE.
Final published version