State of the art in shape matching
Principles of visual information retrieval
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Ground Truth Correspondence Measure for Benchmarking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A quadratic programming based cluster correspondence projection algorithm for fast point matching
Computer Vision and Image Understanding
Parametric active contour model by using the honey bee mating optimization
Expert Systems with Applications: An International Journal
Point Set Registration via Particle Filtering and Stochastic Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
AntShrink: Ant colony optimization for image shrinkage
Pattern Recognition Letters
Bee colony optimization for the p-center problem
Computers and Operations Research
Expert Systems with Applications: An International Journal
Image edge detection using variation-adaptive ant colony optimization
Transactions on computational collective intelligence V
Robust contour matching via the order-preserving assignment problem
IEEE Transactions on Image Processing
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Bee colony optimization (BCO) is a meta-heuristic technique inspired by natural behavior of the bee colony. In this paper, the BCO technique is exploited to tackle the shape matching problem with the aim to find the matching between two shapes represented via sets of contour points. A number of bees are used to collaboratively search the optimal matching using a proposed proximity-regularized cost function. Furthermore, the proposed cost function considers the proximity information of the matched contour points; this is in the contrast to that these contour points are treated independently in the conventional approaches. Experimental results are presented to demonstrate that the proposed approach is able to provide more accurate shape matching than the conventional approaches.