Learning Parts-Based Representations of Data
The Journal of Machine Learning Research
Learning user intention in relevance feedback using optimization
Proceedings of the international workshop on Workshop on multimedia information retrieval
Efficient MRF deformation model for non-rigid image matching
Computer Vision and Image Understanding
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
International Journal of Computer Vision
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
Approaches and Challenges for Cognitive Vision Systems
Creating Brain-Like Intelligence
Foreground Focus: Unsupervised Learning from Partially Matching Images
International Journal of Computer Vision
Label to region by bi-layer sparsity priors
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Shape-from-recognition: Recognition enables meta-data transfer
Computer Vision and Image Understanding
Patch Growing: Object segmentation using spatial coherence of local patches
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Patch Growing: Object segmentation using spatial coherence of local patches
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Spatial Configuration of Local Shape Features for Discriminative Object Detection
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
From Images to Shape Models for Object Detection
International Journal of Computer Vision
International Journal of Computer Vision
Delving deeper into the whorl of flower segmentation
Image and Vision Computing
Modeling user feedback using a hierarchical graphical model for interactive image retrieval
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Non-rigid image registration using graph-cuts
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
From region based image representation to object discovery and recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Localizing objects while learning their appearance
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A unified contour-pixel model for figure-ground segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ClassCut for unsupervised class segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Object recognition with hierarchical stel models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Figure-ground image segmentation helps weakly-supervised learning of objects
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
International Journal of Computer Vision
Non-rigid image registration of brain magnetic resonance images using graph-cuts
Pattern Recognition
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Estimating image segmentation difficulty
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets
International Journal of Computer Vision
Towards unsupervised discovery of visual categories
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
International Journal of Computer Vision
Located hidden random fields: learning discriminative parts for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
Learning a generative model of images by factoring appearance and shape
Neural Computation
Label-to-region with continuity-biased bi-layer sparsity priors
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes
International Journal of Computer Vision
Weakly Supervised Localization and Learning with Generic Knowledge
International Journal of Computer Vision
Knowledge leverage from contours to bounding boxes: a concise approach to annotation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
A boosting approach for the simultaneous detection and segmentation of generic objects
Pattern Recognition Letters
Multi-view K-means clustering on big data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
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We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and pose. A key aspect of this model is that the object appearance is allowed to vary from image to image, allowing for significant within-class variation. By iteratively updating the belief in the object驴s position, size, segmentation and pose, LOCUS avoids making hard decisions about any of these quantities and so allows for each to be refined at any stage. We show that LOCUS successfully learns an object class model from unlabeled images, whilst also giving segmentation accuracies that rival existing supervised methods. Finally, we demonstrate simultaneous recognition and segmentation in novel images using the learned models for a number of object classes, as well as unsupervised object discovery and tracking in video.