Genetic algorithms for automatic classification of moving objects

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. Categories and Subject Descriptors: I.2.6 [Artificial Intelligence]: Learning--Parameter learning General Terms: Algorithms.

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Omid David-Tabibi, Nathan S. Netanyahu, Yoav Rosenberg, Moshe Shimoni
• Keywords: Automatic Object Classification, GECCO 2010, Nearest Neighbor Classification, Optimization, State-of-the-art Commercial Object