Proceedings of the 1998 conference on Advances in neural information processing systems II
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Affine Invariant Clustering and Automatic Cast Listing in Movies
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
ImageGrouper: Search, Annotate and Organize Images by Groups
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Image Modeling with Position-Encoding Dynamic Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Unsupervised Learning of Object Features from Video Sequences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Hierarchical Statistical Learning of Generic Parts of Object Structure
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Context and Hierarchy in a Probabilistic Image Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Dependent Regions for Object Categorization in a Generative Framework
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Semantic-Shift for Unsupervised Object Detection
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Unsupervised identification of multiple objects of interest from multiple images: dISCOVER
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A robust approach for object recognition
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Learning hierarchical shape models from examples
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A successful representation of objects in literature is as a collection of patches, or parts, with a certain appearance and position. The relative locations of the different parts of an object are constrained by the geometry of the object. Going beyond a single object, consider a collection of images of a particular scene category containing multiple (recurring) objects. The parts belonging to different objects are not constrained by such a geometry. However, the objects themselves, arguably due to their semantic relationships, demonstrate a pattern in their relative locations. Hence, analyzing the interactions among the parts across the collection of images can allow for extraction of the foreground objects, and analyzing the interactions among these objects can allow for a semantically meaningful grouping of these objects, which characterizes the entire scene. These groupings are typically hierarchical. We introduce hierarchical semantics of objects (hSO) that captures this hierarchical grouping. We propose an approach for the unsupervised learning of the hSO from a collection of images of a particular scene. We also demonstrate the use of the hSO in providing context for enhanced object localization in the presence of significant occlusions, and show its superior performance over a fully connected graphical model for the same task.