Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
On Affine Invariant Clustering and Automatic Cast Listing in Movies
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
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 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
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
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
Semantic-Shift for Unsupervised Object Detection
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
CVPR'04 Proceedings of the 2004 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
3D Object Modeling and Segmentation Based on Edge-Point Matching with Local Descriptors
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
3D Object Mapping by Integrating Stereo SLAM and Object Segmentation Using Edge Points
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Unsupervised Learning for Graph Matching
International Journal of Computer Vision
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Given a collection of images of offices, what would we say we see in the images? The objects of interest are likely to be monitors, keyboards, phones, etc. Such identification of the foreground in a scene is important to avoid distractions caused by background clutter and facilitates better understanding of the scene. It is crucial for such an identification to be unsupervised to avoid extensive human labeling as well as biases induced by human intervention.Most interesting scenes contain multiple objects of interest. Hence, it would be useful to separate the foreground into the multiple objects it contains. We propose dISCOVER, an unsupervised approach to identifying the multiple objects of interest in a scene from a collection of images. In order to achieve this, it exploits the consistency in foreground objects - in terms of occurrence and geometry - across the multiple images of the scene.