The nature of statistical learning theory
The nature of statistical learning theory
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Geometry and texture recovery of scenes of large scale
Computer Vision and Image Understanding
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conceptual spatial representations for indoor mobile robots
Robotics and Autonomous Systems
Instance-based AMN classification for improved object recognition in 2D and 3D laser range data
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dialog-based 3D-image recognition using a domain ontology
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Representation and classification of 3-D objects
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba's Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.