Pattern matching algorithms
A tree-edit-distance algorithm for comparing simple, closed shapes
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection and Localization by Dynamic Template Warping
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Cubist Approach to Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Non-Parametric Method for Capturing Non-Rigid Deformations
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Shape retrieval based on dynamic programming
IEEE Transactions on Image Processing
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We present a novel approach for measuring image similarity based on the composition of parts. The measure identifies common sub-regions between the images at multiple sizes, and evaluates the amount of deformation required to align the common regions. The scheme allows complex, non-rigid deformation of the images, and penalizes irregular deformations more than coherent shifts of larger sub-parts. The measure is implemented by an algorithm which is a variant of dynamic programming, extended to multi-dimensions, and is using scores measured on a relative scale. The similarity measure is shown to be robust to non-rigid deformations of parts at various positions and scales, and to capture basic characteristics of human similarity judgments.