Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Image Modeling with Position-Encoding Dynamic Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Machine Learning
International Journal of Computer Vision
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Shape Guided Object Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Smooth image segmentation by nonparametric bayesian inference
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning to combine bottom-up and top-down segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Video topic modelling with behavioural segmentation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Real-Time Object Segmentation Using a Bag of Features Approach
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
Special Issue on Probabilistic Models for Image Understanding, Part II
International Journal of Computer Vision
Notation-Invariant patch-based wall detector in architectural floor plans
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Object class detection: A survey
ACM Computing Surveys (CSUR)
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This paper addresses the problem of accurately segmenting instances of object classes in images without any human interaction. Our model combines a bag-of-words recognition component with spatial regularization based on a random field and a Dirichlet process mixture. Bag-of-words models successfully predict the presence of an object within an image; however, they can not accurately locate object boundaries. Random Fields take into account the spatial layout of images and provide local spatial regularization. Yet, as they use local coupling between image labels, they fail to capture larger scale structures needed for object recognition. These components are combined with a Dirichlet process mixture. It models images as a composition of regions, each representing a single object instance. Gibbs sampling is used for parameter estimations and object segmentation.Our model successfully segments object category instances, despite cluttered backgrounds and large variations in appearance and viewpoints. The strengths and limitations of our model are shown through extensive experimental evaluations. First, we evaluate the result of two methods to build visual vocabularies. Second, we show how to combine strong labeling (segmented images) with weak labeling (images annotated with bounding boxes), in order to limit the labeling effort needed to learn the model. Third, we study the effect of different initializations. We present results on four image databases, including the challenging PASCAL VOC 2007 data set on which we obtain state-of-the art results.