Paper Info
Title
Feature extraction and evolution based pattern recognition
Abstract
This paper proposes a novel method of classifier selection for efficient object recognition based on evolutionary computation and data context knowledge called Evolvable Classifier Selection. The proposed method tries to distinguish the data characteristics of input image (data contexts) and selects a classifier system accordingly using the genetic algorithm. It stores its experiences in terms of the data context category and the artificial chromosome so that the context knowledge can be accumulated and used later. The proposed method operates in two modes: the evolution mode and the action mode. It can improve its performance incrementally using GA in the evolution mode. Once sufficient context knowledge is accumulated, the method can operate in real-time. The proposed method has been evaluated in the area of face recognition. Data context-awareness, modeling and identification of input data as data context categories, is carried out using SOM.
Year
DOI
Venue
2006
10.1007/11816157_101
ICIC (1)
Keywords
Field
DocType
data context knowledge,data context category,novel method,input data,evolution mode,feature extraction,pattern recognition,data context-awareness,context knowledge,data context,data characteristic,real time,object recognition,genetic algorithm,face recognition,evolutionary computing
Data modeling,Facial recognition system,Evolutionary algorithm,Pattern recognition,Computer science,Evolutionary computation,Feature extraction,Context model,Artificial intelligence,Classifier (linguistics),Genetic algorithm
Conference
Volume
ISSN
ISBN
4113
0302-9743
3-540-37271-7
Citations 
PageRank 
References 
0
0.34
14
Authors
2
Name
Order
Citations
PageRank
Mi Young Nam16115.03
Phill Kyu Rhee26024.82