DocumentCode :
2027202
Title :
An incremental evolutionary method for optimizing dynamic image retrieval systems
Author :
Nikzad, Mohammad ; Moghaddam, Hamid Abrishami
Author_Institution :
Sci. & Res. Branch, Islamic Azad Univ., Tehran, Iran
fYear :
2010
fDate :
27-28 Oct. 2010
Firstpage :
1
Lastpage :
4
Abstract :
This paper introduces a new incremental evolutionary optimization method based on evolutionary group algorithm (EGA). The EGA was presented as an approach to overcome time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing retrieval (CBIR) optimization tasks. Here, we consider another challengeable limitation of usual evolutionary learning and optimization systems: learning in the scale-varying and dynamic environments. Hence, we present a new strategy based on EGA that is enhanced with the ability of incremental learning. Evaluation results on scale-varying and simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.
Keywords :
evolutionary computation; image retrieval; learning (artificial intelligence); optimisation; EGA; dynamic image retrieval system; evolutionary algorithm; evolutionary learning; incremental evolutionary method; optimization system; scale varying environment; simulated dynamic CBIR system; time consuming drawback; Biological cells; Evolutionary computation; Genetic algorithms; Heuristic algorithms; Indexing; Optimization; Quantization; Content-Based Image Indexing and Retrieval; Evolutionary Algorithms (EAs); Incremental Learning; Wavelet Correlogram;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Vision and Image Processing (MVIP), 2010 6th Iranian
Conference_Location :
Isfahan
Print_ISBN :
978-1-4244-9706-5
Type :
conf
DOI :
10.1109/IranianMVIP.2010.5941133
Filename :
5941133
Link To Document :
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