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. © 2011 Springer-Verlag.
CITATION STYLE
David, O., Netanyahu, N. S., & Rosenberg, Y. (2011). Genetic algorithms for automatic object movement classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6935 LNCS, pp. 258–265). https://doi.org/10.1007/978-3-642-24082-9_32
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