A New Evolutionary Algorithm for Optimizing Content Based Image Indexing Methods

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. Contrary to genetic algorithms (GA), the image database is partitioned into several smaller subsets in the new evolutionary algorithm and each subset is used by an updating process as training patterns for each chromosome during evolution. Each EGA chromosome includes an age gene that implies the progress of the updating process, evolutionary genes that participate in evolution and history genes that save previous information of the updating process. Also, a new fitness function is defined to evaluate the fitness of the chromosomes 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, average rank, and standard deviation of rank for the wavelet correlogram algorithm.