TY - GEN
T1 - Genetic algorithms for automatic object movement classification
AU - David, Omid
AU - Netanyahu, Nathan S.
AU - Rosenberg, Yoav
PY - 2011
Y1 - 2011
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 - Movement classification
KW - Parameter tuning
UR - http://www.scopus.com/inward/record.url?scp=80054088716&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24082-9_32
DO - 10.1007/978-3-642-24082-9_32
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AN - SCOPUS:80054088716
SN - 9783642240812
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 258
EP - 265
BT - Convergence and Hybrid Information Technology - 5th International Conference, ICHIT 2011, Proceedings
T2 - 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011
Y2 - 22 September 2011 through 24 September 2011
ER -