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An incremental evolutionary method for optimizing dynamic image retrieval systems

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2 Author(s)
Mohammad Nikzad ; Islamic Azad University Science and Research branch, Tehran, Iran ; Hamid Abrishami Moghaddam

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.

Published in:

2010 6th Iranian Conference on Machine Vision and Image Processing

Date of Conference:

27-28 Oct. 2010