Edge affinity for pose-contour matching
Computer Vision and Image Understanding
POP: Patchwork of Parts Models for Object Recognition
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape matching and registration by data-driven EM
Computer Vision and Image Understanding
Efficient Learning of Relational Object Class Models
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Figure-ground separation by cue integration
Neural Computation
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th international conference on Machine learning
Object detection by global contour shape
Pattern Recognition
An Effective Data Processing Method for Fast Clustering
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Contour Grouping with Partial Shape Similarity
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Latent mixture vocabularies for object categorization and segmentation
Image and Vision Computing
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
Shape-Based Object Localization for Descriptive Classification
International Journal of Computer Vision
Labeling Still Image Databases Using Graphical Models
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Data Discovery and Related Factors of Documents on the Web and the Network
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
MM '09 Proceedings of the 17th ACM international conference on Multimedia
CoCRF deformable model: a geometric model driven by collaborative conditional random fields
IEEE Transactions on Image Processing
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
POSIT: Part-based object segmentation without intensive training
Pattern Recognition
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
Distance Learning Based on Convex Clustering
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Comparison and analysis of segmentation techniques in pattern analysis and machine intelligence
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
An effective detection method for clustering similar XML DTDs using tag sequences
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Scene image segmentation based on perceptual organization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detecting object boundaries using low-, mid-, and high-level information
Computer Vision and Image Understanding
Deformable probability maps: Probabilistic shape and appearance-based object segmentation
Computer Vision and Image Understanding
A context-based region labeling approach for semantic image segmentation
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
What can we learn from biological vision studies for human motion segmentation?
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning and incorporating top-down cues in image segmentation
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
Located hidden random fields: learning discriminative parts for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Learning compositional categorization models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Object categorization by compositional graphical models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
Shape-Based Object Detection via Boundary Structure Segmentation
International Journal of Computer Vision
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
A boosting approach for the simultaneous detection and segmentation of generic objects
Pattern Recognition Letters
Object class detection: A survey
ACM Computing Surveys (CSUR)
Learning discriminative localization from weakly labeled data
Pattern Recognition
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
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In this work we show how to combine bottom-up and top-down approaches into a single figure-ground segmentation process. This process provides accurate delineation of object boundaries that cannot be achieved by either the top-down or bottom-up approach alone. The top-down approach uses object representation learned from examples to detect an object in a given input image and provide an approximation to its figure-ground segmentation. The bottom-up approach uses image-based criteria to define coherent groups of pixels that are likely to belong together to either the figure or the background part. The combination provides a final segmentation that draws on the relative merits of both approaches: The result is as close as possible to the top-down approximation, but is also constrained by the bottom-up process to be consistent with significant image discontinuities. We construct a global cost function that represents these top-down and bottom-up requirements. We then show how the global minimum of this function can be efficiently found by applying the sum-product algorithm. This algorithm also provides a confidence map that can be used to identify image regions where additional top-down or bottom-up information may further improve the segmentation. Our experiments show that the results derived from the algorithm are superior to results given by a pure top-down or pure bottom-up approach. The scheme has broad applicability, enabling the combined use of a range of existing bottom-up and top-down segmentations.