Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Adaptive mixtures of local experts
Neural Computation
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Image modeling using tree structured conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Detecting object boundaries using low-, mid-, and high-level information
Computer Vision and Image Understanding
Non-local characterization of scenery images: statistics, 3D reasoning, and a generative model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Superpixels and supervoxels in an energy optimization framework
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Turbopixel segmentation using Eigen-images
IEEE Transactions on Image Processing
A conditional random field model for image parsing
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Multiple region categorization for scenery images
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Embedding Gestalt laws on conditional random field for image segmentation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
KDE Paring and a Faster Mean Shift Algorithm
SIAM Journal on Imaging Sciences
Robust text and drawing segmentation algorithm for historical documents
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Object class detection: A survey
ACM Computing Surveys (CSUR)
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Bottom-up approaches, which rely mainly on continuity principles, are often insufficient to form accurate segments in natural images. In order to improve performance, recent methods have begun to incorporate top-down cues, or object information, into segmentation. In this paper, we propose an approach to utilizing category-based information in segmentation, through a formulation as an image labelling problem. Our approach exploits bottom-up image cues to create an over-segmented representation of an image. The segments are then merged by assigning labels that correspond to the object category. The model is trained on a database of images, and is designed to be modular: it learns a number of image contexts, which simplify training and extend the range of object classes and image database size that the system can handle. The learning method estimates model parameters by maximizing a lower bound of the data likelihood. We examine performance on three real-world image databases, and compare our system to a standard classifier and other conditional random field approaches, as well as a bottom-up segmentation method.