Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
A data-driven approach to prior extraction for segmentation of left ventricle in cardiac MR images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Object detection combining recognition and segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Bottom-up and top-down object matching using asynchronous agents and a contrario principles
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Context, Computation, and Optimal ROC Performance in Hierarchical Models
International Journal of Computer Vision
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
Learning-Based symmetry detection in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Graph matching and clustering using kernel attributes
Neurocomputing
An edge detection with automatic scale selection approach to improve coherent visual attention model
Pattern Recognition Letters
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A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach.