Title :
Genetic Algorithm for Silhouette Matching
Author :
Li, Y. ; Suganthan, P.N. ; Qi, X.L. ; Wang, Y.J.
Author_Institution :
Sch. of Optometry, Waterloo Univ., Ont.
Abstract :
Genetic algorithms (GAs) have been applied to matching problem. However, traditional GAs do not perform well in matching problem because there can be many locally similar parts. This paper presents a new genetic algorithm for silhouette matching. New concepts of partially matched gene-strings in the initial population, the extending operator and the order adjustment algorithm are proposed. Each gene-string in the initial population only has three matched points while other points are unmatched. During the evolution, each gene-string will have more matched points due to the applications of the crossover and extending operators. The extending operator determines a potential match for an unmatched point near a matched point by searching the local space. After the application of the crossover and extending operators, the adjustment algorithm enforces each gene-string to be an ordered list by removing some matched points, if necessary. Our experiments show that the new matching algorithm based on GA performs better than traditional GA-based algorithms
Keywords :
feature extraction; genetic algorithms; image matching; image retrieval; genetic algorithm; image retrieval; partially matched gene-strings; shape retrieval; shape similarity; silhouette matching; Biophysics; Clustering algorithms; Data structures; Dynamic programming; Genetic algorithms; Humans; Image retrieval; Information retrieval; Shape measurement; Spatial databases; Genetic algorithm; Image retrieval; Shape retrieval; Shape similarity; Silhouette matching;
Conference_Titel :
Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on
Conference_Location :
Singapore
Print_ISBN :
1-4244-0341-3
Electronic_ISBN :
1-4214-042-1
DOI :
10.1109/ICARCV.2006.345256