Recognizing indoor scenes
Author(s)Quattoni, Ariadna; Torralba, Antonio
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Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g, bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information. In this paper we propose a prototype based model that can successfully combine both sources of information. To test our approach we created a dataset of 67 indoor scenes categories (the largest available) covering a wide range of domains. The results show that our approach can significantly outperform a state of the art classifier for the task.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009.
Institute of Electrical and Electronics Engineers
Quattoni, A., and A. Torralba. “Recognizing indoor scenes.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 413-420. © Copyright 2009 IEEE
Final published version
INSPEC Accession Number: 10836098