A novel evolutionary approach for optimizing content-based image indexing algorithms.
Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.
Saadatmand-Tarzjan
Mahdi
M
Electrical Engineering Department, K.N. Toosi University of Technology, 16315 Tehran, Iran. saadatmand@kiaeee.org
Moghaddam
Hamid Abrishami
HA
D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D032701:Biomimetics / Q000379:methods; D003628:Database Management Systems; D016208:Databases, Factual; D004282:Documentation / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*