TY - GEN
T1 - Genetic algorithms for automatic classification of moving objects
AU - David-Tabibi, Omid
AU - Netanyahu, Nathan S.
AU - Rosenberg, Yoav
AU - Shimoni, Moshe
PY - 2010
Y1 - 2010
N2 - This paper presents an integrated approach, combining a state-of-the-art commercial object detection system and genetic algorithms (GA)-based learning for automatic object classification. Specifically, the approach is based on applying weighted nearest neighbor classification to feature vectors extracted from the detected objects, where the weights are evolved due to GA-based learning. Our results demonstrate that this GA-based approach is considerably superior to other standard classification methods.
AB - This paper presents an integrated approach, combining a state-of-the-art commercial object detection system and genetic algorithms (GA)-based learning for automatic object classification. Specifically, the approach is based on applying weighted nearest neighbor classification to feature vectors extracted from the detected objects, where the weights are evolved due to GA-based learning. Our results demonstrate that this GA-based approach is considerably superior to other standard classification methods.
KW - Automatic object recognition
KW - Computer vision
KW - Genetic algorithms
KW - Parameter tuning
UR - http://www.scopus.com/inward/record.url?scp=77955976628&partnerID=8YFLogxK
U2 - 10.1145/1830761.1830866
DO - 10.1145/1830761.1830866
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:77955976628
SN - 9781450300735
T3 - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
SP - 2069
EP - 2070
BT - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
T2 - 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Y2 - 7 July 2010 through 11 July 2010
ER -