A fast parametric maximum flow algorithm and applications
SIAM Journal on Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Model Order Selection and Cue Combination for Image Segmentation
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
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Computer Vision and Image Understanding
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Combined Top-Down/Bottom-Up Segmentation
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
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
A Combinatorial Solution for Model-Based Image Segmentation and Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large linear classification when data cannot fit in memory
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation by figure-ground composition into maximal cliques
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Beyond bounding-boxes: learning object shape by model-driven grouping
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Extracting 3d scene-consistent object proposals and depth from stereo images
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
Object class detection: A survey
ACM Computing Surveys (CSUR)
Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition
International Journal of Computer Vision
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We present an approach to visual object-class segmentation and recognition based on a pipeline that combines multiple figure-ground hypotheses with large object spatial support, generated by bottom-up computational processes that do not exploit knowledge of specific categories, and sequential categorization based on continuous estimates of the spatial overlap between the image segment hypotheses and each putative class. We differ from existing approaches not only in our seemingly unreasonable assumption that good object-level segments can be obtained in a feed-forward fashion, but also in formulating recognition as a regression problem. Instead of focusing on a one-vs.-all winning margin that may not preserve the ordering of segment qualities inside the non-maximum (non-winning) set, our learning method produces a globally consistent ranking with close ties to segment quality, hence to the extent entire object or part hypotheses are likely to spatially overlap the ground truth. We demonstrate results beyond the current state of the art for image classification, object detection and semantic segmentation, in a number of challenging datasets including Caltech-101, ETHZ-Shape as well as PASCAL VOC 2009 and 2010.