Genetic Algorithms for Automatic Classification of Moving Objects

O David-Tabibi; N. S Netanyahu; Y Rosenberg; M Shimoni

Genetic Algorithms for Automatic Classification of Moving Objects

O David-Tabibi, N. S Netanyahu, Y Rosenberg, M Shimoni

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	American English
Title of host publication	The 12th annual conference companion on Genetic and evolutionary computation
Publisher	ACM
State	Published - 2010

Bibliographical note

Place of conference:Portland, Oregon, USA

Cite this

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title = "Genetic Algorithms for Automatic Classification of Moving Objects",

abstract = "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.",

author = "O David-Tabibi and Netanyahu, {N. S} and Y Rosenberg and M Shimoni",

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year = "2010",

language = "American English",

booktitle = "The 12th annual conference companion on Genetic and evolutionary computation",

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T1 - Genetic Algorithms for Automatic Classification of Moving Objects

AU - David-Tabibi, O

AU - Netanyahu, N. S

AU - Rosenberg, Y

AU - Shimoni, M

N1 - Place of conference:Portland, Oregon, USA

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.

UR - https://scholar.google.co.il/scholar?q=Genetic+Algorithms+for+Automatic+Classification+of+Moving+Objects&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - The 12th annual conference companion on Genetic and evolutionary computation

PB - ACM

ER -

Genetic Algorithms for Automatic Classification of Moving Objects

Abstract

Bibliographical note

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