Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
The Journal of Machine Learning Research
Image Parsing: Unifying Segmentation, Detection, and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Surface Layout from an Image
International Journal of Computer Vision
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
What Is a Good Image Segment? A Unified Approach to Segment Extraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Category independent object proposals
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
On parameter learning in CRF-based approaches to object class image segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Random Fourier approximations for skewed multiplicative histogram kernels
Proceedings of the 32nd DAGM conference on Pattern recognition
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
Exploiting inference for approximate parameter learning in discriminative fields: an empirical study
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Figure/Ground assignment in natural images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Sequential Figure-Ground Ranking
International Journal of Computer Vision
Approximating the maximum weight clique using replicator dynamics
IEEE Transactions on Neural Networks
Semantic segmentation using regions and parts
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Image segmentation by figure-ground composition into maximal cliques
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Layered Object Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation over detection by coupled global and local sparse representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Semantic segmentation with second-order pooling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Learning Hierarchical Features for Scene Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
We propose a layered statistical model for image segmentation and labeling obtained by combining independently extracted, possibly overlapping sets of figure-ground (FG) segmentations. The process of constructing consistent image segmentations, called tilings, is cast as optimization over sets of maximal cliques sampled from a graph connecting all non-overlapping figure-ground segment hypotheses. Potential functions over cliques combine unary, Gestalt-based figure qualities, and pairwise compatibilities among spatially neighboring segments, constrained by T-junctions and the boundary interface statistics of real scenes. Building on the segmentation layer, we further derive a joint image segmentation and labeling model (JSL) which, given a bag of FGs, constructs a joint probability distribution over both the compatible image interpretations (tilings) composed from those segments, and over their labeling into categories. The process of drawing samples from the joint distribution can be interpreted as first sampling tilings, followed by sampling labelings conditioned on the choice of a particular tiling. We learn the segmentation and labeling parameters jointly, based on maximum likelihood with a novel estimation procedure we refer to as incremental saddle-point approximation. The partition function over tilings and labelings is increasingly more accurately approximated by including incorrect configurations that are rated as probable by candidate models during learning. State of the art results are reported on the Berkeley, Stanford and Pascal VOC datasets, where an improvement of 28 % was achieved for the segmentation task only (tiling), and an accuracy of 47.8 % was obtained on the test set of VOC12 for semantic labeling (JSL).